dirk van oosterhout 21.06.2016 use of mwd data for ... - unit
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Dirk van Oosterhout 21.06.2016
Use of MWD data for detecting discontinuities
Use of MWD data for detecting discontinuities By
D. van Oosterhout
in partial fulfilment of the requirements for the degree of
Master of Science in Geo-Engineering
at the Delft University of Technology,
to be defended publicly on Thursday June 30, 2016 at 12:00.
Supervisor: Dr. Ir. D.J.M. Ngan-Tillard TU Delft Thesis committee: Prof. Dr. G. Bertotti TU Delft
Dr. J. Benndorf, TU Delft Prof. Dr. A. Bruland NTNU Dr. Ir. P.D. Jakobsen NTNU Ir. M.L Arntsen, Norwegian Public Roads Administration
An electronic version of this thesis will available at http://repository.tudelft.nl/.
I
Abstract
In the last decades, measurement while drilling, or MWD, technology has set foot in the drill and blast tunnelling industry. Penetration rate, thrust, torque pressure, percussive pressure, rotation speed, water flow and water pressure are registered on a centimetre scale and processed to parameters more dependent on geology. These parameters could ideally be used to adapt the blast design for a more efficient blast and to predict the amount of rock support needed. In reality, MWD technology is often only used to document geology. The main reasons for this could be that the workflow is not fully adapted to the MWD technology, the knowledge about MWD is not sufficient or the ability of MWD to represent the geology has not been extensively investigated and verified for drill and blast tunnelling. MWD has great potential since it is a relatively cheap and simple method of collecting a larger amount of data, which does not interfere with the workflow in drill and blast tunnelling. The aim is to investigate the ability to detect discontinuities and their geometrical properties in MWD data and to evaluate the usability of MWD data in terms of detecting discontinuities. The first of five objectives is a literature review on the subjects of MWD technology, discontinuities in rock masses, previous research and statistical data analysis methods. Raw MWD parameters are processed and filtered by software, which can somewhat be seen as eliminating influences such as depth dependency and percussive pressure. This results in modified penetration rate, torque or water pressure which represents rock mass properties. Previous research showed the inability to compare MWD data and available geological reporting about fractures was due to a large difference in scale. For statistical analysis of MWD data in this study 4 methods are considered. Principle component analysis and k-means cluster analyses are forms of unsupervised learning and can potentially help to understand and reduce complexity of multivariate datasets. Linear and logistic regressions are forms of supervised learning that can give insight into predictability and potentially predict the presence discontinuities. The second objective is to gather data, which is appropriate and detailed enough to study relations between MWD data and discontinuities. Two tunnels under construction are visited where limestone and highly foliated phyllite are the dominant rock types. These are the Solbakktunnelen and Bjørnegårdtunnelen. While not disturbing the construction work, different methods of detailed geological mapping are used. Blasthole remains used as scanlines for mapping discontinuities and mapping discontinuities in the contour and face led to a dataset reflecting the geological situation in tunnels. Another method for mapping discontinuities, which is not influenced as much by blasting, is borehole inspection. The use of an inspection camera and an optical televiewer resulted in 11 video footages and 25 detailed recordings of 5 meter long boreholes. The third objective is to evaluate the data by a visual comparisons of geological data and MWD data. Comparing the mapped geology and 3D images of MWD data showed that fractures with a certain infill or aperture are visible in MWD data. The more detailed geological data showed that an open fracture or a fracture with soft infill and an aperture wider than 1cm often leads to a peak in penetration rate, rotation pressure, processed penetration and processed rotation pressure. The fourth objective is to apply statistical methods to come to a more in depth understanding of the relation between MWD data and discontinuities and confirm findings in objective 3. Since it is suspected that only the actual location and aperture can be predicted, a vector is made by assigning the number 1 to each MWD sampling depth between the upper and lower boundary of a discontinuity. For intact rock, a 0 is assigned to each MWD sampling depth. The principle component analyses showed that the dataset could be reduced to a manageable number of 5 to 7 components and that around 80% of the variability was retained. K-means cluster analyses is found to be an appropriate analysis for 2 out of 4 datasets. It led to the understanding that responses in MWD data due to lithological discontinuities without an aperture cannot be separated from intact rock. Training the data with logistic regression
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analyses confirmed this finding. Logistic regression did however differentiate MWD data of open fractures from other MWD data in half of the collected data. 2 out of 3 fractures were predicted, but the exact location and size of predicted fractures differ slightly from the real location. The regression for the fractures with soft infill was successful for a quarter of the data. 6 out of 9 fractures with clayey infill were predicted, again with a small deviation in size. Testing the regression equations on test datasets, which were not part of the input for data training, did not lead to the correct prediction of fractures. The last objective is to discuss the current and future usability of MWD data for the Norwegian Public Roads Administration. Even though the statistical analyses did not fully succeed in separating responses of fractures in MWD data, visually responses are found to be characteristic. The fact that not all discontinuities give a distinctive response, makes calculations of rock quality designation unreliable. Therefore, in terms of rock support, MWD data can only assist in decision making concerning spot bolting to secure wedges due to large fractures. This research might contribute to help understand and predict grout volumes. The use of 3D images of MWD data could give a better understanding of the in situ fracture structure. Ideally, this knowledge can be used to anticipate possible under- and overbreak due to these fractures before blasting. Considering the findings in this study, it is still presumed that MWD technology has great potential even if it might not lead to the prediction of each type of discontinuity.
III
Acknowledgments
During this project I got help of many different parties and individuals. Firstly I would like to thank every
Norwegian for showing patience with my Norwegian language and not shifting to English too easily. You
have contributed a great deal to my Norwegian language skills.
I would like to thank the team from the Norwegian Public Roads Administration working on the
Bjørnegårdtunnelen and Marco Filipponi from Marti AS at the Solbakktunnelen site. Not only has
gathering of data been essential for my thesis, it was special to learn about tunnel construction outside
lecture rooms.
The help of Thorvald Wetlesen from Bever Control AS has been crucial to get to this result. Thanks for
your openness in MWD processing method, assistance with gathering MWD data and support with data
management. In addition, I would like to thank Harald Elvebakk from NGU for driving a long way to
provide the televiewer service.
Essential for me in writing this thesis have been five academics and one engineer from the Norwegian
Public Roads Administration. I would like to thank Amund Bruland, Pål Drevland Jakobsen and Mari Lie
Arntsen for providing data and contacts, taking care of finances, giving valuable feedback and much
more. It means a great deal to me to be welcomed as an outsider and be given this much trust. At TU
Delft I would like to thank Giovanni Bertotti for support in the geological field and Jörg Benndorf for
support with data analysis. I want to express special thanks to Dominique Ngan-Tillard who has been
my direct supervisor. She has supported me through the last years in my choices of academic ambitions.
Overall, the supervision has been perfect, giving me the freedom to steer my own project.
To finish I would like to express my enormous gratitude towards my family and Kari. Thanks!
Delft, University of Technology Dirk van Oosterhout
June 5th, 2016
IV
Table of content
Abstract .................................................................................................................................................... I
Acknowledgments .................................................................................................................................. III
Table of content ..................................................................................................................................... IV
List of figures ........................................................................................................................................ VIII
List of tables ............................................................................................................................................ X
1. Introduction ......................................................................................................................................... 1
1.1 MWD technology in drill and blast tunnelling ............................................................................... 1
1.2 Drill and blast tunnelling ................................................................................................................ 1
1.3 The Norwegian Public Roads Administration ................................................................................ 2
1.4 Problem definition, aim and objectives ......................................................................................... 3
1.5 Research questions ....................................................................................................................... 4
1.6 Limitations 5
1.7 Scope and constraints ................................................................................................................... 6
1.8 Thesis outline ................................................................................................................................ 6
2. Literature study ................................................................................................................................... 7
2.1 MWD technology........................................................................................................................... 7
2.2 Discontinuities and discontinuity mapping .................................................................................... 7
2.3 The drilling process ........................................................................................................................ 8
2.4 MWD responses to discontinuities ................................................................................................ 9
2.5 Common practise of geological mapping in Norwegian tunnels ................................................... 9
2.6 Data processing ........................................................................................................................... 10
2.7 Multivariate statistics .................................................................................................................. 13
2.7.1 Principle Component Analysis .............................................................................................. 13
2.7.2 K-means cluster analysis ....................................................................................................... 15
2.7.3 Multiple linear regression analysis ....................................................................................... 16
2.7.4 Multiple logistic regression................................................................................................... 17
2.7.5 Overview of statistical analyses ............................................................................................ 20
2.8 Correlation between MWD and geology ..................................................................................... 20
2.8.1 Grouting volumes and MWD data ........................................................................................ 20
2.8.2 Geological boundaries and zones of weaknesses ................................................................. 21
2.8.3 Mechanical properties of rock and MWD data..................................................................... 22
2.8.4 Mapped geology and MWD data .......................................................................................... 22
2.8.5 Findings in research in the field of MWD technology ........................................................... 22
V
3. Data gathering and collected data ................................................................................................ 23
3.1 Preparation of fieldwork at Bjørnegårdtunnelen ........................................................................ 23
3.1.1 Project .................................................................................................................................. 23
3.1.2 Geology ................................................................................................................................ 23
3.2 Preparation of fieldwork at Solbakktunnelen .............................................................................. 24
3.2.1 Project .................................................................................................................................. 24
3.2.2 Geology ................................................................................................................................ 24
3.3 Geological mapping ..................................................................................................................... 25
3.3.1 Tunnel wall mapping ............................................................................................................ 25
3.3.2 Borehole inspection .............................................................................................................. 26
3.4 Fieldwork at Bjørnegardtunnelen ................................................................................................ 26
3.4.1 Tunnel wall mapping and MWD data ................................................................................... 27
3.4.2 Borehole inspection and MWD data .................................................................................... 30
3.4.3 Overview of collected data ................................................................................................... 34
3.5 Fieldwork at Solbakktunnelen ..................................................................................................... 34
3.5.1 Tunnel wall mapping and MWD data ................................................................................... 34
3.5.2 Borehole inspection and MWD data .................................................................................... 35
3.5.3 Overview of collected data ................................................................................................... 36
4. Visual validity assessment of MWD data ........................................................................................... 37
4.1 Tunnel wall mapping data ........................................................................................................... 37
4.1.1 Overview of observations in MWD data and mapped geology ............................................ 39
4.2 Televiewer data ........................................................................................................................... 39
4.2.1 Data preparation .................................................................................................................. 39
4.2.2 Borehole 1 ............................................................................................................................ 40
4.2.3 Borehole 2 ............................................................................................................................ 40
4.2.4 Borehole 3 ............................................................................................................................ 41
4.2.5 Borehole 4 ............................................................................................................................ 41
4.2.6 Borehole 5 ............................................................................................................................ 42
4.2.7 Borehole 6 ............................................................................................................................ 42
4.2.8 Borehole 7 ............................................................................................................................ 42
4.2.9 Borehole 8 ............................................................................................................................ 43
4.2.9 Borehole 9 and 10 ................................................................................................................ 43
4.2.10 Borehole 11 to 15 ............................................................................................................... 43
4.2.11 Borehole 16 to 20 ............................................................................................................... 44
4.2.12 Borehole 21 to 25 ............................................................................................................... 45
4.2.13 Overview of observations in MWD data and mapped geology with televiewer ................. 46
VI
4.3 Borehole camera data ................................................................................................................. 46
5. Statistical validity assessment of MWD data ..................................................................................... 48
5.1 Data sets 48
5.2 Principle component analysis ...................................................................................................... 50
5.2.1 Determining the number of principle components .............................................................. 50
5.2.2 Orthogonal and oblique rotation .......................................................................................... 51
5.2.3 PCA outcome dataset 1 ........................................................................................................ 52
5.2.4 PCA outcome dataset 2 ........................................................................................................ 55
5.2.5 PCA outcome dataset 3 ........................................................................................................ 55
5.2.6 PCA outcome dataset 4 ........................................................................................................ 57
5.2.7 Overview of PCA analyses ..................................................................................................... 59
5.3 Cluster analysis ............................................................................................................................ 59
5.3.1 K-means cluster analysis ....................................................................................................... 59
5.3.2 Cross tabulation clusters and discontinuity groups .............................................................. 60
5.3.2.1 Dataset 1 ....................................................................................................................... 60
5.3.2.2 Dataset 2 ....................................................................................................................... 62
5.3.2.2 Dataset 3 and 4.............................................................................................................. 62
5.3.3 Overview of cluster analysis outcome .................................................................................. 63
5.4 Logistic regression ....................................................................................................................... 63
5.4.1 Training dataset 1 ................................................................................................................. 63
5.4.1.1 All discontinuities .......................................................................................................... 63
5.4.1.2 Discontinuities with an aperture>0 ............................................................................... 64
5.4.1.3 Discontinuities with an aperture>0 and no infill ............................................................ 64
5.4.2 Training dataset 2 ................................................................................................................. 65
5.4.2.1 All discontinuities .......................................................................................................... 65
5.4.2.2 Discontinuities with an aperture>0 ............................................................................... 65
5.4.2.3 Discontinuities with an aperture>0 and no infill ............................................................ 66
5.4.3 Training dataset 3 ................................................................................................................. 66
5.4.3.1 All discontinuities .......................................................................................................... 66
5.4.3.2 Discontinuities with an aperture>0 ............................................................................... 66
5.4.3.3 Discontinuities with an aperture>0 and no infill ............................................................ 66
5.4.4 Training dataset 4 ................................................................................................................. 67
5.4.4.1 Discontinuities and with an aperture>0 ........................................................................ 67
5.4.4.2 Discontinuities with an aperture>0 and soft or no infill ................................................ 67
5.4.4.3 Discontinuities with an aperture>0 and no infill ............................................................ 68
5.4.5 Summary of logistic regression analyses .............................................................................. 68
VII
5.5 Regression equation .................................................................................................................... 69
5.5.1 Testing regression outcome from dataset 1 ......................................................................... 69
5.5.2 Testing regression outcome from dataset 2 ......................................................................... 70
5.5.3 Testing regression outcome from dataset 3 ......................................................................... 70
5.5.4 Testing regression outcome from dataset 4 ......................................................................... 70
5.5.5 Overview of predicted discontinuities .................................................................................. 70
6. Discussion .......................................................................................................................................... 71
6.1 Literature study ........................................................................................................................... 71
6.2 Data gathering and collected data .............................................................................................. 71
6.3 Visual validity assessment of MWD data ..................................................................................... 71
6.4 Statistical validity assessment of MWD data ............................................................................... 72
6.5 Current usability of MWD ............................................................................................................ 74
7. Conclusion and recommendations .................................................................................................... 75
Research questions ........................................................................................................................... 75
Recommendations ............................................................................................................................ 77
Bibliography .......................................................................................................................................... 79
VIII
List of figures
Figure 1. Illustration of the drill and blast cycle, starting in the top with drilling and loading and
continuing with blasting, ventilating, mucking, scaling, installing rock support and surveying (Heiniö,
1999). ...................................................................................................................................................... 2
Figure 2. Top view of tunnelling sites and tunnel section with in the top Solbakktunnelen and below the
Bjørnegårdtunnelen (The Norwegian Public Roads Administration, 2014). ............................................ 4
Figure 3. Illustration of primary geometrical properties of discontinuities in rock (Hudson & Harrison,
1997). ...................................................................................................................................................... 8
Figure 4. Illustration of feed, rotation and percussion impact for the top hammer drilling method (Olsen
V. , 2009). ................................................................................................................................................ 9
Figure 5. 2D documentation of discontinuities by the Norwegian Public Roads Administration (Lillevik,
2011). .................................................................................................................................................... 10
Figure 6. Two dimensional illustration of Principle Component Analysis (Powell & Lehe, u.d.). ........... 14
Figure 7. Illustration of binary outcome in a linear regression scatter plot. .......................................... 17
Figure 8. The logit function of logistic regression. ................................................................................. 17
Figure 9. Illustration of how Hjelme placed boreholes in a spread out tunnel contour sheet for
comparing MWD data to mapped geology. .......................................................................................... 21
Figure 10. Map of southern Norway with tunnel site locations. ........................................................... 23
Figure 11. Cross sections of geology encountered with constructing the two tunnel profiles (GEOVITA,
2014). .................................................................................................................................................... 24
Figure 12. Geological map with Solbakktunnelen section, the yellow line is approximately 2km
(Multiconsult, 2009). ............................................................................................................................. 25
Figure 13. Illustration of an optical televiewer (NGU, 2015). ................................................................ 26
Figure 14. Mapping at tunnel wall in tunnel A at profile 955 of an injection borehole remain. Person
length is 1.75m. ..................................................................................................................................... 27
Figure 15. Graphical representation of calcite infilled discontinuity in tunnel A at profile 505. ........... 28
Figure 16. Graphical approximation of positions of discontinuities seen along tunnel face 975 in tunnel
tube A. ................................................................................................................................................... 28
Figure 17. View along the tunnel face 975 tunnel A. The packers are separated by approximately 1.2m
(top left), the packers stick approximately 30 to 40 cm out of the wall (top right), the rock bolts in the
shotcrete have a diameter of 20cm (lower right).................................................................................. 29
Figure 18. Approximate location holes 1 and 2 drilled with angles of 20° to 30° from the tunnel axis. 30
Figure 19. Approximate location holes 3 to 10 drilled with angles of 55° and 90° from the tunnel axis.
.............................................................................................................................................................. 31
Figure 20. Approximate location holes 11 to 15 close to 975 and holes 16 to 20 close to 960, drilled with
angles of 40° to 60° from the tunnel axis. ............................................................................................. 31
Figure 21. Approximate location holes 21 to 25 drilled with angles of 20° to 30° from the tunnel axis.
.............................................................................................................................................................. 32
Figure 22. Graph showing drops in feeder pressure due to jamming of the drill bit caused by difficulties
with aligning drilling direction of the large hole. ................................................................................... 33
Figure 23. The inspection camera and portable computer. .................................................................. 35
Figure 24. Screenshots of typical video footages. ................................................................................. 36
Figure 25. View from underneath the tunnel, or the tunnel floor, of RMS normalised penetration with
a moving average of 4 values showing possible locations of mapped discontinuities. ......................... 38
Figure 26. Top view of RMS normalised penetration with a moving average of 4 values of the contour
showing possible locations of mapped discontinuities. ........................................................................ 38
IX
Figure 27. Illustration of mismatch between televiewer depth and MWD data. .................................. 46
Figure 28. Zones with light coloured minerals which might give a response in MWD data. Left in
borehole 1 at a depth of 4.8m and right in borehole 4 at a depth of 3.5m. .......................................... 47
Figure 29. Scree plot for dataset 1 to determine the number of statistically significant components. . 51
X
List of tables
Table 1. Over view of statistical analyses. ............................................................................................. 20
Table 2. Geometrical properties of discontinuities crossing an injection borehole remain close to profile
955 in tunnel A. ..................................................................................................................................... 27
Table 3. Overview of collected data at the Bjørnegårdtunnelen. .......................................................... 34
Table 4. Overview of collected data at the Solbakktunnelen. ............................................................... 36
Table 5. Overview of observations in MWD data and mapped geology. ............................................... 39
Table 6. Overview of observation in MWD data and geological data from the televiewer inspections.47
Table 7. Table showing how depth sampling of discontinuity location vector is done. ......................... 49
Table 8. Component correlation matrices with an oblique rotation for all datasets. Highlighted in green
are the highest Pearson correlations..................................................................................................... 51
Table 9. Total variance explained by principle components. ................................................................. 53
Table 10. Pattern matrix giving the correlation between parameters and principle components for
dataset 1. ............................................................................................................................................... 54
Table 11. Pattern matrix giving the correlation between parameters and principle components for
dataset 2. ............................................................................................................................................... 56
Table 12. Pattern matrix giving the correlation between parameters and principle components for
dataset 3. ............................................................................................................................................... 57
Table 13. Pattern matrix giving the correlation between parameters and principle components for
dataset 4. ............................................................................................................................................... 58
Table 14. Overview of PCA analyses. ..................................................................................................... 59
Table 15. The outcome of Variance Ration Criterion. ........................................................................... 59
Table 16. Cross tabulation of MWD clusters and discontinuity groups for dataset 1. ........................... 61
Table 17. Cross tabulation of principle component clusters and discontinuity groups for dataset 1. ... 61
Table 18. Cross tabulation of MWD clusters and discontinuity groups for dataset 2. ........................... 62
Table 19. Cross tabulation of PC clusters and discontinuity groups for dataset 2. ................................ 63
Table 20. Overview if cluster analysis outcome. .................................................................................... 63
Table 21. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0
and no infill. ........................................................................................................................................... 65
Table 22. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0.
.............................................................................................................................................................. 65
Table 23. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0
and no infill. ........................................................................................................................................... 66
Table 24. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0
and no infill. ........................................................................................................................................... 67
Table 25. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0
and soft or no infill. ............................................................................................................................... 68
Table 26. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0
and no infill. ........................................................................................................................................... 68
Table 27. Overview of the best predicting models which meet the Wald statistics and Hosmer and
Lemeshow test. ..................................................................................................................................... 69
Table 28. Overview of predictions in the test datasets. ........................................................................ 70
1
1. Introduction
In this first chapter readers will be introduced to MWD technology, tunnelling methods and the
company which assigned this thesis. Next, the problem statement and objectives will be given followed
by research questions and limitations.
1.1 MWD technology in drill and blast tunnelling
In the beginning of the 20th century, Schlumberger pioneered with downhole logging for the oil industry
with the aim of increasing the understanding of geology in oil fields. In the 1970’s the technology was
introduced in the mining industry to detect ore bodies (Valli, 2010). In the last decades MWD has also
set foot in the tunnelling industry and since 1997 MWD tools are equipped standardly on drilling rigs.
MWD stands for measurement while drilling and it is in principle the registration of drilling parameters.
These drilling parameters are processed. The raw and processed drilling parameters are interpreted to
predict geology and improve drilling efficiency, this is often called Drill Parameter Interpretation. MWD
technology in tunnelling leads to large amounts of data. Nowadays computers and software packages
make it possible to process this data and visualize it in 3D. Ideally, these outcomes are used to adapt
the blast design for a more efficient blast and to predict the amount of rock support needed (Nilsen &
Palmstrom, 2013). In reality, it is often only used to document geology.
Nevertheless, MWD technology has great potential as it is a relatively cheap and simple method of
collecting a larger amount of data, which does not intervene with the workflow in drill and blast
tunnelling. In the next section the use of MWD technology in drill and blast tunnelling is introduced.
1.2 Drill and blast tunnelling
Two commonly used tunnelling techniques are tunnelling with a tunnel boring machine and drill and
blast tunnelling. Both involve measurement while drilling, but this thesis is merely focussed on drill and
blast tunnelling. Drill and blast tunnelling consists of a series of actions, where each cycle typically ends
with a 5 meter tunnelling advance depending on rock mass quality and blast vibrations. It starts with
drilling blast holes, usually of 5m long. Several tens of holes are drilled, but an average number of holes
is hard to give since the number of holes depend on the rock, wanted perfection of tunnel contour and
tunnel dimensions. MWD data is collected for each blast hole. The next step is charging and blasting.
After ventilation to reduce dust and dangerous gas concentrations, the blasted rock is transported out
of the tunnel by trucks or a conveyor belt. At this stage the rock mass might still be unstable so the rock
surfaces are scaled by machine and hand to remove loose hanging blocks. After this, it is up to the
geologist or engineering geologist and the contractor to decide what kind of rock support will be used
to support the rock on the long term. In Norway this is often called ‘byggherrens halvtime’, which
directly translated means ‘the client’s half hour’. It is a contracted activity where no other activities
should take place than geological mapping and deciding on rock support. The next step in this process
is the installation of the rock support, which can be bolts, reinforced ribs of sprayed concrete, spiling
bolts, reinforced shotcrete or a combination of these support types. Often these are installed directly
after removal of blasted rock. In some cases these types of rock support are temporary. Permanent
rock support might be the installation of additional rock support or cast in place concrete lining. The
final step is a survey to prepare the next cycle. The cycle is illustrated in Figure 1. Tunnelling is an
expensive activity, time efficiency is therefor of the essence.
2
As will be discussed later, this thesis will be mainly focused on the detection of discontinuities. If
discontinuities can be identified during probe or blast hole drilling this could help in different fields.
Fracturing has great influence on the rock mass quality and therefore the amount of rock support
needed. For example, a fractured rock with small discontinuity spacing can require multiple forms of
rock support like shotcrete, bolts, spiling bolts etc. A rock mass with multiple discontinuity sets could
lead to the formation of wedges, which can collapse. Large wedges can be supported by spot bolting.
Another form of rock support is injection of grout. Grout injection is done to increase the water
tightness. Large volumes of grout are injected in fractures in rock masses. The understanding of where
and how rocks are fractured could potentially help to predict the volume of grout that is needed.
Another example when detection of large discontinuities can be useful is to prevent under- and
overbreak. Excavating too much or too little rock would require additional shotcrete or reblasts to get
the desired tunnel contour. Fracturing in the rock mass is one of the causes of under- and overbreak.
To summarize, detection of discontinuities before excavation would help to characterize rock masses
early and to anticipate to these rock mass conditions before excavation.
1.3 The Norwegian Public Roads Administration
The Norwegian Public Roads Administration, or NPRA, is responsible for planning, construction and
operation of the national and county road network. This road network also includes about 1000 road
tunnels with an added length of about 800km. Two tunnels which are assigned by the NPRA have been
visited to collect data for this thesis: Solbakktunnelen and Bjørnegårdtunnelen. In Figure 2 illustrations
of both tunnels are given. The Solbakktunnel will be a 14.3km tunnel, which will make it world-longest
subsea road tunnel with the deepest point at 290m below sea level. The Bjørnegårdtunnel will be a
2.3km tunnel and it is built to reduce traffic in Sandvika. This thesis is written in collaboration with the
Norwegian Public Roads Administration.
Figure 1. Illustration of the drill and blast cycle, starting in the top with drilling and loading and continuing with blasting, ventilating, mucking, scaling, installing rock support and surveying (Heiniö, 1999).
3
1.4 Problem definition, aim and objectives
MWD technology in drill and blast tunnelling is rather new. Like mentioned before, it is often only used
to document geology while it has the potential to improve the drill and blast tunnelling process. The
main reasons for this could be that MWD technology is not fully adapted to the drill and blast workflow,
the knowledge about MWD is not sufficient or the ability of MWD to represent the geology has not
been extensively investigated and verified. Apart from rock hardness, the little amount of MWD
research in fracturing and water conditions have not shown overwhelming results. Concerning
fracturing, one main obstacle, which has been encountered in previous research, is the difference in
scale between MWD data and available geological information. MWD data has a typical sampling
interval of 2 to 5cm. The most detailed geological information available from tunnel sites is often
mapped geology by engineering geologist. Since the objective of this geological mapping is to document
geology and agree on rock support types, a cm scale accuracy is not relevant and far too time consuming
in the drill and blast tunnelling. Only large structures like intrusions, rock type changes and large
discontinuities are mapped. This is often done by different engineers and locations of structures are
estimated. This might be sufficient for the objective of the geological mapping, but for comparing
geology and MWD data, this is too inaccurate and unprecise. The Norwegian Public Roads
Administration spends money on facilitating MWD software and MWD services, while the ability of
some parts of MWD technology has not been verified. The Norwegian Public Roads Administration
defines the use of MWD for their projects in their process code (The Norwegian Public Roads
Administration, Prosesskode 1 - Standard beskrivelsetekster for vegkontrakter, hovedprosess 1-7.,
2012). It is stated that all drilling rigs must be equipped with logging equipment and used for at least
90% of the borehole length.
The aim of this thesis is to investigate the ability to detect discontinuities and their geometrical
properties in MWD data and to evaluate the usability of MWD data in terms of detecting discontinuities
for the NPRA as the client in future tunnelling projects.
Five objectives are established for this project.
1. Firstly, the subjects of MWD technology, discontinuities in rock masses and previous research
need to be studied. In addition, the literature study should be focussed on possible statistical
data analysis methods, which can be used to evaluate MWD data.
2. The second objective is to gather data, which is appropriate to study relations between MWD
data and discontinuities. This objective will include preparation and execution of fieldwork.
3. The third objective is to evaluate the data by visual comparisons of geological data collected
during fieldwork and MWD data.
4. The fourth objective will be to apply statistical methods to come to a more in depth
understanding of the relation between MWD data and discontinuities and confirm findings in
objective 3.
5. The last objective will be to discuss the current and future usability of MWD data for the NPRA.
4
1.5 Research questions
The main research question is: To what extent can discontinuities be detected by MWD data and used for reporting geology in a tunnel, predicting geology ahead of the tunnel face and predicting the amount of rock support needed? This will be answered by achieving the listed objectives and answering the following research questions.
1. Conduct a literature study of the subject MWD technology, discontinuities in rock masses, statistical analysis that can be used to evaluate MWD data and previous research in the subject of MWD validity. - What is MWD technology in drill and blast tunnelling? - What kind of discontinuities can be encountered? - How is geology mapped in tunnels by the Norwegian Public Roads Administration? - How is MWD data filtered and processed? - What statistical analyses are appropriate for analysing MWD data to enhance and
understand the predictability of discontinuities? - What is the result of previous research on the validity and usability of MWD?
Figure 2. Top view of tunnelling sites and tunnel section with in the top Solbakktunnelen and below the Bjørnegårdtunnelen (The Norwegian Public Roads Administration, 2014).
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2. Prepare site visits and conduct fieldwork. - What is the local geology of the tunnel sites? - What methods can be used to map discontinuities in tunnels? - What are the parameters to obtain during fieldwork? - To what extent is it possible to work on sites where the degree of fracturing is the only
variable parameter? - What is the quality of the collected data?
3. Assess the validity of MWD data by visually comparing the geological data and MWD data. - What kind of discontinuities are visible in MWD data or what kind of geometrical properties
make discontinuities visible in MWD data? - Is there a characteristic response of MWD data to discontinuities?
4. Assess the validity of MWD data through statistical analyses. - What geological data can be used for statistical analyses? - How should geological data be translated to statistical input? - Are the statistical analyses appropriate for the MWD data collected? - Can discontinuities and/or certain geometrical properties be predicted by use of statistical
methods? 5. Discuss the current and future usability of MWD data for the Norwegian Public Roads
Administration and contractors - To what extent are discontinuities visible in MWD data? - In which way could the outcome of this study be used to improve the process of drill and
blast tunnelling ? - What significance do detected discontinuities have for rock support determination?
1.6 Limitations
In advance it can be stated that performing fieldwork in tunnels under construction does not allow
much freedom. An outsider is limited in where he or she can be mapping geology and how much time
he or she may get. Both client and contractor are bound to contracts, which involve large amounts of
money. It is therefore unwanted to disturb the tunnelling processes. By good communication and
planning, these limitation will be minimized.
Next to that the conditions under which geological mapping is done in a tunnel are different from above
ground. Artificial light, accessibility and noise could influence the ability of gathering data. As much as
possible, these limitations should be taken into account when designing data gathering methods and
preparing fieldwork. Moreover, there is always a possibility that rock masses do not show a wide variety
of discontinuities. This limitation cannot be foreseen.
The most important limitation is that no matter how interesting a fresh rock contour or face might be,
it cannot be studied in an unsafe situation, both in terms of rock stability and construction activities.
This will always be discussed before entering tunnels with client and contractor and during data
gathering with staff.
A limitation with the use of MWD technology is that a misconception might rise that MWD data gives
all the information you need to know about rock mass conditions. This is incorrect. Geological mapping
gives a much better idea of the variability of the geology than MWD technology can. As a result, the
aim of this study is not to replace geological mapping, but to support the geologist and improve drill
and blast tunnelling.
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1.7 Scope and constraints
This thesis is mainly focused on the detection and prediction of discontinuities from MWD data.
Objective 5, the actual usefulness of the results of this study, is covered in much less detail. Although it
would be interesting, the scope of this study does not cover the implications of discontinuity detection
on the drill and blast workflow and costs. It also does not aim to predict discontinuities based on MWD
data for an entire tunnelling project. The main reason that this study does not aim to cover the two
mentioned points is that these require extensive knowledge about the extent to which discontinuities
can be detected. The available sources of information do not give this knowledge. In fact, none of the
available resources attempted to study discontinuities and MWD data as detailed as is attempted with
this study.
1.8 Thesis outline
This thesis starts with a general introduction to MWD technology, including problem definition, aim and
objectives, research questions and limitations. Chapter 2 provides background information of all the
different aspects involved in this project. MWD technology, discontinuities and discontinuity mapping,
the drilling process, common practise of geological mapping in Norwegian tunnels, data analysis,
multivariate statistics and previous research in the correlation between MWD technology and geology
is summarized in the literature study.
Chapter 3 includes introductions to the tunnel sites visited for data gathering: Solbakktunnelen and Bjørnegårdtunnelen. This is followed by descriptions of the preparations for data gathering, a short summary of the fieldwork and the results of the fieldwork. Next, the data is analysed. In chapter 4 this is done visually. Here, a mismatch between scales of geological mapping and MWD data is still allowed. In chapter 5, only the detailed geological records are used for a statistical analysis. In the discussion the potential of MWD technology in the field of fracturing is discussed. In the conclusion the findings and answers to the research questions will be summarized. Furthermore, the main limitations and their possible solutions will be discussed and recommended.
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2. Literature study
Here, the literature study on the subjects MWD technology, discontinuities in rock, the drilling process,
geological mapping in Norwegian tunnels, data processing, statistical analyses and previous research in
the field of MWD is summarized. The 4 main sources of literature are the library at NTNU, the library at
TU Delft, a database of the Norwegian Public Roads Administration and the internet.
2.1 MWD technology
Like stated, MWD technology deals with recording drilling parameters and processing these
parameters. In the past it used to be only penetration rate, which was measured, whereas today the
systems installed on the drilling rigs register 9 parameters: Penetration rate, thrust, torque pressure,
percussive pressure, rotation speed, water flow, water pressure, depth and sampling time. The drilling
rigs and measuring systems are calibrated either at the site or by use of previous projects. The
parameters are filtered and processed by software to visualize measures of the hardness, fracturing
and water conditions. Some software packages put more focus on defining these three outcomes, while
other software leaves it to the user to interpret the different outcomes.
The amount of data collected with one advance round can be enormous. For example, if one advance
round consists of 80 blast holes, all 9 parameters plus 6 processed parameters are stored. With a
common sampling interval of 2cm, this gives 300000 data points.
2.2 Discontinuities and discontinuity mapping
A discontinuity is defined by Hudson and Harrison as ‘’any separation in the rock continuum having
effectively zero tensile strength.’’ (Hudson & Harrison, 1997). Hudson and Harrison state that
discontinuities can be the single most important factor governing the deformability, strength and
permeability. Therefore, discontinuities can critically effect the stability of a tunnel. There are different
type of discontinuities. In this thesis, four different types of discontinuities are considered (Shanghal &
Gupta, 2010).
1. Lithological discontinuities, the layering in sedimentary rock.
2. Foliation, discontinuous planes caused by parallel alignment of minerals due to stress.
3. Fracture, discontinuous planes where stress caused partial loss of cohesion.
4. Faults and shear zones, discontinuities caused by movement due to shear stress.
5. Other geological discontinuities, for example intrusive contact and veins.
In addition, there can be blast induced fractures present.
Several geometrical properties are defined. These include spacing, orientation, persistence,
termination, roughness, aperture, discontinuity sets and presence of water (see Figure 3).
Spacing is the distance between discontinuity intersections with a scanline. Orientation is defined as
the dip direction, the horizontal angle between the normal of the discontinuity plane and north, and
dip angle of the plane. Persistence is the length or size of a discontinuity plane. Termination is the
ending of a fracture due to another fracture. Aperture is the perpendicular distance between two
adjacent discontinuities.
In practise, it would be too time consuming and costly to map these properties for all discontinuities in
a tunnel under construction. Several classification systems are designed to classify the quality of the
rock. In the Norwegian tunnelling industry, the Q-system is used. The Q-systems gives a value between
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0.001 and 1000, which indicates the quality of the rock (NGI, 2013). This value can also be used to
estimate the amount of rock support needed. The Q-value is a computation of six different parameters:
𝑄 =𝑅𝑄𝐷
𝐽𝑛∗
𝐽𝑟
𝐽𝑎∗
𝐽𝑤
𝑆𝑅𝐹
Rock Quality Designation, or 𝑅𝑄𝐷, is the sum of the length of all core pieces longer than 10 cm as a
percentage of the total length of the core. It can also be estimated by counting the number of fractures
per m3. 𝐽𝑛 is the joint set number, which represents the number of joint sets and random joints.
Together the first term represents the relative block size. 𝐽𝑟 is the joint roughness number representing
both small scale roughness (rough, smooth and slickenside) and large scale roughness (stepped,
undulating and planar). 𝐽𝑎 represents the joint friction or shear strength. The second term is an
approximation of the actual friction angle. 𝐽𝑤 is the joint water reduction factor which represents the
presence of water and 𝑆𝑅𝐹 is the stress reduction factor which represents the relation between rock
strength and stress.
2.3 The drilling process
In the drill and blast tunnelling industry the drilling method used is top hammer drilling. It is a
combination of applying feed, rotation and percussion impact transmitted through the drilling steel
(Figure 4) (Olsen V. , 2009). All the energy is transferred through the drilling string. The drill bit is
covered with several indenters, which are either small hemispheres or cones. If an indenter is forced in
to the rock, the stress in the rock increases and a zone around the indenter is deformed plastically. A
small zone directly under the indenter is crushed to rock powder. Just outside the zone where rock is
pulverized the stress can reach the peak strength and the rock can break. In a larger hemisphere around
the indenter, cracks are induced and some of these cracks can find a path out to the free surface. This
is called chip formation (Olsen V. , 2009). The indenters on a drilling bit are forced in the rock by the
percussion pressure. The position of the indenters is changed continuously by the rotation and the
drilling bit is kept in contact with the rock by the feed pressure.
Thrust, rotation speed and percussion pressure are parameters which are controlled by the operator
or automatic drilling system. Therefore, these parameters are independent. The drilling system is
programmed to drill as fast as possible without jamming the drill bit. Jamming can for example be
Figure 3. Illustration of primary geometrical properties of discontinuities in rock (Hudson & Harrison, 1997).
Termination
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caused by a too high thrust. In that case, the contact of the drill bit with the rock is continuous and
rotation and indentation are obstructed. The dependent parameters are penetration rate and torque.
These are dependent on the independent parameters and are the possible cause of variation.
2.4 MWD responses to discontinuities
The drilling behaviour when crossing an open discontinuity has been researched. Schunnesson
summarises the findings of Barr in 1984 in his PhD thesis (Schunnesson H. , 1996). Most or all of the
following events happened in an artificial set-up of rock blocks including discontinuities with known
orientation aperture and infilling.
When a discontinuity was met, the following was observed:
- A short increase in penetration rate.
- A small increase in rotational speed followed by a sharp decrease when re-establishing rock
contact.
- A drop in torque pressure followed by sharp increase on re-establishing rock contact.
- A drop in water pressure until equal pressure in the void or rock is established.
- A drop in feed pressure followed by a sharp increase on re-establishing rock contact. This might
only be visible for discontinuities with a large aperture.
An infilled fracture can dampen the events. The most reliable event is the increase in penetration rate.
Schunnesson found in his own research that the intensity of fracturing can cause the penetration rate,
torque pressure and rotational speed to increase.
2.5 Common practise of geological mapping in Norwegian tunnels
In the Norwegian tunnelling industry, it is common that geological inspection and documentation is
done by a geologist or an engineering geologist employed by the client. After the removal of blasted
rock and the cleaning of the surfaces for the application of shotcrete, the client has a 30 to 45 minutes
window, in which there is no other activity in the tunnel, to document the rock mass properties
(Rongved, u.d.). In common practise this means the documentation of the Q-values, classification of
rock types, mapping of fractures. This is an interactive process between client and contractor. When
rock mass quality is high, the geological mapping is often done during operation and the drill and blast
activities are not put on a hold. Afterwards the client stores the information digitally. The Norwegian
Public Roads Administration uses the program Novapoint from Vianova Systems AS which gives the
options to document foliation, discontinuities, joint sets, joints with infill, weakness zones < 1m,
weakness zones > 1m, rock type, Q values and water leakage (Vianova Systems AS, 2011). Novapoint
gives a 2 dimensional representation of the exposed tunnel contour, meaning that the (engineering)
geologist has to document in 2 dimensions. The tunnel contour is split in four parts, shown as dashed
Figure 4. Illustration of feed, rotation and percussion impact for the top hammer drilling method (Olsen V. , 2009).
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lines parallel to the tunnel axis in Figure 5. The tunnel floor is not included in mapping. The geological
features are transformed from the 3 dimensional space to the 2 dimensional as illustrated in Figure 5.
The Norwegian Public Roads Administration defines this work facet in their process code (The
Norwegian Public Roads Administration, Prosesskode 1 - Standard beskrivelsetekster for vegkontrakter,
hovedprosess 1-7., 2012). Documentation and mapping of the geology is done to help planning rock
support installation, to have documentation of geology and to see the link between geology and
installed rock support.
2.6 Data processing
The common practise of processing MWD data is based on a doctoral thesis written by Schunnesson.
Schunnesson aimed to develop a method for analysing MWD data and reducing the need for calibration
of rock properties against MWD data (Schunnesson H. , 1996) (Schunnesson H. , 1997) (Schunnesson &
Holme, 1997) (Schunnesson & Sturk, 1997). The data used in his thesis came from the Viscaria mine,
the Zinkgruvan mine, the Glodberget tunnel and the Hallandsåsen tunnel. Schunnesson proposed a
method to separate the two main groups of parameters, namely the independent parameters, which
are feeder pressure, rotation speed and percussion pressure, and dependent parameters, which are
penetration rate and torque pressure. First, the length dependency along the boreholes should be
taken into account for all parameters. Length dependent behaviour is caused by for example the
reflection of energy with the addition of each drilling rod, increase in friction with the rock and the
decrease of flushing efficiency. Next, the applied differences in thrust should be normalized for
penetration rate and torque pressure. Finally, the influence of penetration rate on torque should be
normalized. The resulting data consists of systematic variations caused by the drill rig itself. This should
be subtracted from the original data set to remain with the unsystematic variations or rock mass
variation. A straight forward way to do this is to withdraw the gradient of the trend line of every dataset
from every dataset.
Figure 5. 2D documentation of discontinuities by the Norwegian Public Roads Administration (Lillevik, 2011).
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Schunnesson stated that after applying this method, only the rock dependent variations are left in the
data. He found that the magnitude of penetration rate and torque pressure generally are good
indicators for rock hardness and that both increase in fractured rock. However, responses based on the
magnitude of penetration rate and torque pressure differ from site to site. The variability of penetration
rate and torque pressure usually has a more general relation to fracturing. Schunnesson gave several
examples from his data that showed this. He used principle component analyses (PCA) to investigate
the correlation between penetration rate and torque pressure. For the Glodberget tunnel,
Schunnesson showed that less fractures gave a smoother curve.
There are a number of software packages which process the raw MWD data. Both normalisation and
root mean square calculations, abbreviated as RMS, are used to separate dependent and independent
variables and to amplify responses. In contrast with most software developers, Bever Control AS is open
about how raw MWD data is used to get to the three standard evaluations (hardness, fracturing and
water conditions (Håkonsen & Wetlesen, 2008) (Bever Team as, 2009)). Developers sometimes
explicitly define hardness based on a modified penetration rate, fracturing based on a modified torque
pressure and water conditions based on both modified water pressure and water flow. The developers
of Bever Team 3 leave interpretation up to the user and they do not assign the terms hardness,
fracturing or water conditions. Instead, the outcome is called normalized penetration, normalized
rotation pressure, normalized water, RMS normalized penetration, RMS normalized rotation pressure
and RMS normalized water. All outcome is expressed as %. These parameters are influenced by:
1. External influences like drill bit wear and the number of drilling rods.
2. The dependent parameter percussive pressure
3. Hole depth
4. Rock mass properties
In a way the steps explained below can be seen as eliminating influences 1, 2 and 3 to end up with a
modified penetration rate, torque or water pressure which represents rock mass properties in a better
way. The steps are generally the same for penetration rate, torque and water pressure and for
simplicity, these will be called parameter, or P, in the following steps.
Step 1. Influences which do not contain relevant information about rock properties like adding of drilling
rods are removed from the data. Generally, penetration rates lower than 1 m/min, unrealistic high
values for penetration rate and unrealistic low values for percussion pressures.
Step 2. An attempt is made to normalize the wear of the drill bit and the drilling settings. It is noted in
literature that this cannot be fully successful. The formula used to normalize hammer pressure,
penetration rate, rotation pressure, water pressure and water flow in Bever Team 3 is:
𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 =(𝑃𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝑃𝑚𝑒𝑎𝑛)
𝑃𝑚𝑒𝑎𝑛⁄
In which the mean parameter 𝑃𝑚𝑒𝑎𝑛 is calculated over one single whole borehole length.
Step 3. After this normalization the most significant influencing factors on the penetration rate and
rotation pressure are believed to be percussion pressure, friction between drilling rods and borehole
(or depth dependency) and the rock mass property. Thrust and rotation speed mentioned as dependent
parameters by Schunnesson, are not considered by Bever Team 3. It is assumed that the relation
between the parameter, its rock mass property (𝑅𝑀𝑃) and percussive pressure (PP) is linear.
𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 = 𝑅𝑀𝑃 + 𝑘 ∗ 𝑃𝑃
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The constant 𝑘 is found by plotting the raw parameter, without the peaks of adding a rod, against the
percussion pressure and obtaining the linear trend.
Step 4. With this known constant 𝑘 the rock mass properties, or 𝑅𝑀𝑃, can be calculated by filtering out
the influence of the hammer pressure and the drilling depth from the normalized penetration. The
equation below is the result of rearranging the previous equation plus a correction for the drilling depth.
𝑅𝑀𝑃 = 𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 − 𝑘 ∗ 𝑃𝑃 +𝐶𝐻𝐿
100∗ 𝑠
Where 𝐶𝐻𝐿 is a predefined value for the correction of hole length and 𝑠 the sampling depth. It should
be noted that for an unknown reason this is not done for the normalized rotation pressure.
Step 5. The water parameter 𝑃𝑤𝑎𝑡𝑒𝑟 is calculated with the formula below.
𝑃𝑤𝑎𝑡𝑒𝑟 = 𝑃𝑤𝑎𝑡𝑒𝑟𝑓𝑙𝑜𝑤 ∗ (1 − 𝑃𝑤𝑎𝑡𝑒𝑟𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒2) ∗ 𝑊𝑃𝐹
Where 𝑃𝑤𝑎𝑡𝑒𝑟𝑓𝑙𝑜𝑤 is the water flow in l/min, 𝑃𝑤𝑎𝑡𝑒𝑟𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 the water pressure in bar, and 𝑊𝑃𝐹 is the
water pressure factor which is a predefined value. 𝑃𝑤𝑎𝑡𝑒𝑟𝑓𝑙𝑜𝑤 is not corrected for the depth but for the
number of drilling rods since the water is pumped through all drilling rods which are in use. For example,
drilling at a depth of 7m requires 2 drilling rods of 5 meter. Water is then pumped through 10m.
Step 6. The output given as normalized penetration, normalized rotation pressure and normalized
water, or 𝑃𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑, is calculated with a moving average of 𝑁 values
𝑃𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 = 𝐴 − ∑ 𝑃𝑖
𝑖0+𝑁
𝑖=𝑖0
𝑁⁄
Where 𝐴 is either 𝑅𝑀𝑃, 𝑃𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 or 𝑃𝑤𝑎𝑡𝑒𝑟, 𝑖0 is the index of the value to be
calculated, 𝑃𝑖 is the value of a parameter at index 𝑖.
Step 7. This operation influences the dynamic property of curves. A moving root mean square
calculation is done to represent the fluctuations of rock mass properties better. The formula used for
this is:
𝑃𝑜𝑢𝑡𝑝𝑢𝑡 2,𝑅𝑜𝑜𝑡𝑀𝑒𝑎𝑛𝑆𝑞𝑢𝑎𝑟𝑒
= √∑ (𝑃𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 − �̅�𝑜𝑢𝑡𝑝𝑢𝑡 1,𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑚𝑜𝑣𝑖𝑛𝑔 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑜𝑓 𝑁 𝑣𝑎𝑙𝑢𝑒𝑠)2𝑁
𝑁
N is the number of measurements taken for the root means square computation and it influences how
dynamic the response is to changes in the original curve. If 𝑃𝑜𝑢𝑡𝑝𝑢𝑡 2,𝑅𝑜𝑜𝑡𝑀𝑒𝑎𝑛𝑆𝑞𝑢𝑎𝑟𝑒 𝑤𝑎𝑡𝑒𝑟 is larger than
0.7 or smaller than the original water flow value, it is set to 0.
Bever Team 3 allows the user to display every individual curve of every step and 3 dimensional images
with colour scales. These colour scales can be adjusted manually.
Another method to characterize rock masses is by use of specific energy, the energy required to break
one unit volume of rock (Ghosh, 2015) (Rai, Schunnesson, Lundqvist, & Kamur, 2015) (Ghosh,
Schunnesson, & Kumar, 2014). Ghosh et all and Rai et all had the aim to determine an unique
geotechnical description of the rock mass excavated by rotary core drilling. The description of specific
energy used in their research is 𝐸 =𝐹
𝐴+
2𝜋𝑁𝑇
60𝐴𝑢, in which E is the specific energy, F is the thrust, A is the
area of the cross section of the drill bit, N is the rotary speed, T is torque and u is penetration rate. The
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relation between specific energy and compressive strength of the rock has often been verified and is
found to be best in rock masses with changing conditions. Specific energy has not been introduced in
percussion drilling in tunnelling.
2.7 Multivariate statistics
For this thesis the Bever Team 3 software is used to extract raw and processed MWD data. It is
suspected that the moving averages used as described in section 2.6 influences the ability to detect
discontinuities. Therefore processed MWD parameters are generated with different moving averages.
This makes the number of variables even larger. To handle a large amount of variables it is chosen to
apply multivariate statistical analyses. Wuensch listed a number of available multivariate analyses
(Wuensch, 2013). In addition he stated that it is relatively easy to perform a multivariate analysis, but
not easy to interpret the outcome of such an analysis. Wuensch also warns that for each individual
method, multiple choices are to be made and therefore outcomes of analyses may differ from analyser
to analyser. The outcome of a multivariate analysis for this research should enhance the understanding
of the interdependency of MWD parameters, it should give an overview of the most important
parameters influencing fracturing and it should clarify the type of response of a parameter when
encountering a discontinuity.
There are two principle methods to approach sets of data: Supervised and unsupervised learning
(Bergen & Ioannidis, 2015). Unsupervised learning aims to understand relationships between variables.
Therefore, the input is data without an associated response. Examples of unsupervised learning
methods are dimension reduction and cluster analysis. For this study, one dimension reduction method
and one type of cluster analysis are found to be useful, namely Principle Component Analysis (PCA) and
k-means cluster analysis. The input data for these methods will merely be MWD data. Supervised
learning includes the associated response variable, which for this study are the presence and
geometrical properties of discontinuities. The goal of supervised learning is to generalize to new data.
One type of supervised learning is of special interest for this study, namely regression. A regression
could help to predict responses of new data. More specific for this study, regression could help to
predict discontinuities ahead of the tunnel face. In the following sections, two types of regressions are
discussed. They are multiple linear regression analysis and multiple logistic regression.
At the end, an overview of the advantages, disadvantages and data requirements for this thesis is given.
2.7.1 Principle Component Analysis
Principle component analysis, or PCA, is a form of unsupervised learning. PCA is a widely used and well-
developed statistical method. Schunnesson used PCA to investigate the interdependency of MWD
parameters. Jolliffe describes PCA as: ‘The reduction of the dimensionality of a data set consisting of a
large number of interrelated variables, while retaining as much as possible of the variation present in
the data set’ (Jolliffe, 2002). Wackernagel states that PCA can be used for data compression,
multivariate outlier detection, deciphering a correlation matrix, identifying underlying factors and
detecting intrinsic correlation (Wackernagel, 2003). The interdependent variables are transformed to a
new, uncorrelated set of variables, the principle components. The first few of these principle
components contain most of the variation present in all of the original variables. Schunnesson observed
that the MWD parameters penetration rate and torque pressure preserve most of the information
captured by all the MWD parameters.
Below an image of a hypothetical dataset with 2 parameters is given. In the left graphs shows the
original dataset with parameters x and y. If one would try to compress this dataset by deleting 1
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parameter, the choice of deleting either x or y is not simple. By deleting either of them, a significant
amount of variability is lost as can be seen in the image below the graph.
PCA finds a coordinate system that preserves more of the variation (Powell & Lehe, u.d.). The rotated
axes are called the principle components. In Figure 6, the right graph visualizes the new coordinate
system for the hypothetical dataset. As can be seen below the right graph, the first principle component
contributes most to the variation in the dataset. The second principle component contributes much
less to the variation compared to principle component 1 or x and y. By dropping the second principle
component, the data is compressed and still a fair part of the variability is maintained.
For a higher dimensional dataset this is hard to visualize, but the principle is the same. The first principle
component represents most of the variation. The aim is to choose enough principle components so
that a significant part of the variation is preserved and at the same time to reduce number of variables.
A normal approach to investigating the number of principle components required is to look at the scree
plot. This is a plot of eigenvalues versus the component number. The eigenvalues decrease for an
increasing component number. In the scree plot one would look for the transition where the
eigenvalues are decreasing rapidly to where the eigenvalues are relatively small and of the same size.
This is not always an obvious point. A parallel analysis is a more objective way for determining the
number of components needed (O'Connor, 2000). A common parallel analysis is the Monte Carlo
analysis, where data is generated either randomly based or generated based on permutations of the
raw data. The latter option is more suitable to data which is not normally distributed. If the eigenvalue
of the nth component of the parallel analysis is smaller than the eigenvalue of the nth component of the
original dimension reduction analysis, this nth component is to be considered as statistically significant.
Other input which needs to be defined for a parallel analysis is the variables for the PCA, a desired
number of data parallel sets and a desired percentile for statistical significance testing.
Another important aspect of PCA is the type choice of rotation used to convert the variables in principle
components. There are two type of rotations oblique and orthogonal. Like stated before, PCA creates
an uncorrelated sets of variables, i.e. the principle components. It is therefore straightforward to
choose an orthogonal rotation, which forces the principle components to be uncorrelated and makes
the large loadings larger and the small loadings smaller, either within a component or with a variable.
It is also possible to use an oblique rotation, where the principle components are allowed to be
Figure 6. Two dimensional illustration of Principle Component Analysis (Powell & Lehe, u.d.).
15
correlated with each other. The thought behind it is that an oblique rotation could be orthogonal if that
is the most appropriate solution for the data. If the most appropriate solution is a non-orthogonal
relation between principle components and principle components are slightly correlated, it is also
allowed by an oblique rotation. In most cases there will be some correlation between factors or
components. If the correlation coefficient between principle components is 0.32 or higher, it means
there is an overlap of 10% or more in variance among principle components. If there is no theoretical
reason for using an orthogonal rotation, an oblique rotation is recommended (Brown, 2009). When
using a regression analysis an important reason to not use an oblique rotation is the risk of
(multi)collinearity. This will be explained later in this chapter.
The question of whether a principle component analysis should be used or not is answered by a test
called Kaiser-Meyer-Oklin measure of sampling adequacy, or KMO. This is an evaluation including the
off-diagonal elements of the original correlations and rotated correlations. As a rule of thumb, an
outcome below 0.5 indicates no reason to perform a PCA. The higher above 0.5 the more appropriate
a PCA becomes (Dziuban & Shirkey, 1974). Another method of justifying the use of a PCA is by looking
at the percentage of variance that is being accounted for by the PCA, or communalities. The higher
these percentages, the better.
2.7.2 K-means cluster analysis
A cluster analysis is a type of unsupervised learning, where the goal is to identify and understand
possible clusters in the data. A cluster analysis is designed to partition data into subsets with common
characteristics. There are different types of clustering analysis. A good analysis to assess large amounts
of data is the k-means cluster analysis. With k-means clustering, a data point can only be in one cluster,
whereas with other methods, data points can be in multiple clusters. This is also called hard and soft
boundaries. One important aspect is that the number of clusters in the data needs to be predefined.
Unfortunately there is no good way to decide on the correct number of clusters. Only in special cases
one could decide the correct number. For example if the data exist out of grading digits 0 to 9, the
obvious chose is 10 clusters.
The k-means cluster analysis starts with defining the number of clusters k. Next k random initial cluster
centres are generated in the dataspace. Boundaries of equal distance between cluster centres are
computed and every data point within a boundary of a cluster centre is assigned to the cluster with that
random initial centre point. The next step is that new cluster centres are calculated for each cluster
based on the average of the data points in a cluster. New boundaries of equal distance between the
new cluster centres are computed and all data points are redistributed to the new cluster centre. This
is repeated until the cluster centres are not changing.
In this thesis the following considerations are made to assess the number of clusters.
1. Number of iterations: If it takes a high number of iterations to find stable cluster centres, it
indicates that software has to readjust centres often due to a lack of clustering in the data. A
point of attention is that the higher k is defined, the more iterations are needed.
2. Variance Ratio Criterion (VRC). The criterion is given by
VRC𝑘 = (SS𝑏
K − 1)/(
SS𝑤
𝑁 − 𝐾)
Where SS𝑏 is the between cluster variation, SS𝑤 the within cluster variation, K the number of
clusters and 𝑁 the number of objects. VRC𝑘 is in fact the F-value of an one-way ANOVA. To
determine the correct number of clusters, 𝜔𝑘 should be calculated for every cluster. The
smallest 𝜔𝑘 indicates the best number of clusters. 𝜔𝑘 = (VRC 𝑘+1 − VRC𝑘) − (VRC𝑘 − VRC𝑘−1)
16
The VRC has been proven to work well. The down side is that due to the term VRC𝑘−1 the
minimum number clusters that can be evaluated is 3.
After the evaluation of the best number of clusters, the outcome can be cross tabulated with the
geological information. This might give inside in if a cluster could represent a certain group of input
geological variables.
2.7.3 Multiple linear regression analysis
A multiple linear regression analysis is aimed to create a predictive equation of independent variables
that correlate with a dependent variable with a minimum error in predicting the dependent variable. A
commonly used regression analysis is the linear regression analysis. Let 𝛽𝑛 represents the weights of
the dependent variables 𝑋𝑛 contributing to the output variable �̂�. 𝜀 is the error which is assumed to be
0 for multivariate regression. The end result of a linear regression is given below
�̂� = 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 𝛽𝑛𝑋𝑛 + 𝜀
In principle the method starts with looking for the variable that explains most of the variance in the
dependent variable, 𝑋1. Once found, it moves on to find the variable that explains the second most of
the variance of the dependent variable and so on. In order to construct the linear regression an
observation set of the independent variable 𝑌 is needed. It is not possible to explain 100% of the
variance and a challenge is to decide which variables to include.
For this research 𝑌 could be the presence or geometrical property of discontinuities. This information
needs to be transformed to a vector with the same dimensions and interval as the sampling depth. For
the simplest case of detecting the discontinuities’ positions, this vector needs to be filled with the
information ‘no discontinuity present’ and ‘discontinuity present’ on every sampling interval. A multiple
linear regression is not suited to predict a binary outcome. Like described in literature the method to
predict a binary outcome out of multiple input predictor variables is called a multiple logistic regression
model (Hosmer, Sturduvant, & Lemeshow, 2013). The main difference and reason for using a logistic
regression instead of a linear regression model can be illustrated with the simple linear regression
scatter plot of a binary outcome. If a binary outcome is plotted in a linear model plot, the regression
line is described like:
𝑦 = 𝛽0 + 𝛽1𝑥1 + 𝜀
For a logistic regression the binary outcome will be 0 or 1, illustrated with the points in Figure 7. The
binary outcome is closely related to the probability of the outcome to be between 0 and 1. The formula
can be written as:
𝑃(𝑦 = 1) = 𝑝 = 𝛽0 + 𝛽1𝑥1 + 𝜀
When plotting this regression line, it can be seen that for a low 𝑥1 or a high 𝑥1 there is a chances that
𝑝 can become negative or higher than 1. This is not possible. In addition, the error should be scattered
evenly around the regression line. As illustrated with blue lines in Figure 7, the error is not distributed
evenly around the regression line, namely the error is positive on one side of the regression line and
negative on the other side of the regression line or vice versa. This implicates that there is definitely a
pattern in error distribution. A solution would be to cap the linear model, where values 𝑝 < 0 equal 0
17
and the values 𝑝 > 1 equal 1, and represent the regression with a spline function Figure 7. In the next
part another solution to this problem will be explained in more detail, namely the logistic regression.
2.7.4 Multiple logistic regression
A multiple logistic regression model has 𝑛 independent variables 𝒙 = (𝑥1, 𝑥2, … , 𝑥𝑛) with beta-weights
𝜷 = (𝛽0,𝛽1, … , 𝛽𝑛) and the predicted dependent variable is 𝑔. To solve the problem of negative
probabilities and probabilities larger than one, the next equation is used:
𝑝 =exp (𝛽0 + 𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑛𝑥𝑛)
exp(𝛽0 + 𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑛𝑥𝑛) + 1
The equation satisfies the condition of 0 < 𝑝 < 1. This equation can be rewritten to the logistic
regression equation:
𝑔(𝑥) = ln(𝑝
1 − 𝑝) = 𝛽0 + 𝛽1𝑥1 + 𝛽2𝑥2 + ⋯ + 𝛽𝑛𝑛
This equation is also called the logit. Even though the probability function is not a linear function of 𝒙,
the transformation is a linear function of 𝒙. For simple logistic regression the regression line would look
like illustrated in Figure 8.
Note that for a simple linear regression the certain beta-value can easily be interpreted. For example
for every unit of 𝑥1 the probability increases with 𝛽1. For a simple logistic regression this evaluation is
less straight forward. In this case for every unit of 𝑥1, the probability increases with 𝑝 = ln(𝑔(𝑥)
1−𝑔(𝑥)),
where most change happens close to the inclination of the regression line.
x
0
p
1
x
0
p
1
Figure 7. Illustration of binary outcome in a linear regression scatter plot.
x
0
p
1
Figure 8. The logit function of logistic regression.
18
The vector 𝜷 is estimated with the method of maximum likelihood, a method that ‘yields values for the
unknown parameters that maximize the probability of obtaining the observed set of data.’
The assumptions or conditions for the multiple logistic regression analysis are:
1. The explanatory variable can be both continuous, ordinal and nominal independent variables
(Hosmer, Sturduvant, & Lemeshow, 2013).
2. The output variable must be a nominal dependent variable (Hosmer, Sturduvant, & Lemeshow,
2013).
3. The sample size needs to be significant (Hosmer, Sturduvant, & Lemeshow, 2013).
4. There cannot be outliers (Hosmer, Sturduvant, & Lemeshow, 2013).
5. There should be a linear relationship between any continuous independent variable and the
logit transformation of dependent variable (Lund Research Ltd, 2013), i.e. the data should fit
the model.
6. There should be no multicollinearity, i.e. correlations or multiple correlations of sufficient
magnitude which have the adversely affect regression estimates (Lund Research Ltd, 2013).
For the previously discussed input and output, the first 2 conditions are fulfilled. For a single 5 meter
long hole and a sampling interval of 2cm, there will be 250 samples. This is significant and MWD data
of multiple holes might be merged. The last three conditions need to be checked while doing the
analysis. Most likely there will be multicollinearity between the processed MWD data and possibly
between the raw MWD data. PCA might decrease the risk of multicollinearity. Collinearity can lead to
high r2 values. At the same time, if the independent variables are not statistically significant, it can lead
to unrealistic beta-weights with the opposite signs or in the worst case the analysis will not find a
solution. Four checks for multicollinearity will be considered in this thesis. The first check is looking for
extremely high Pearson’s correlation coefficients between the input variables. In literature it is stated
that if a Pearson correlation is close to 0.8, collinearity is likely to exist (Unknown, 2007). The second
check is to see if plus and minus signs of Pearson’s correlation coefficients are the same for beta-
weights. The third check is looking at the magnitude of the standard errors, which in the case of
multicollinearity might be double the size of the other standard errors. The fourth check is called the
tolerance. It is a multiple regression analysis where the input variable is regressed on to the other input
variables. Tolerance is defined as 1 minus the r2 value. Tolerance values less than 0.2 most likely indicate
multicollinearity, tolerance values of less than 0.1 indicate significant multicollinearity. To solve
multicollinearity the variable simply has to be left out or sample size can be increased. The last step
before running the analysis is to decide which parameters to include in the analysis. The final step is to
look at the Pearson’s correlations. Software indicates if a correlation is significant at the 0.01 and 0.05
level.
After establishing the beta-coefficients the model needs to be studied for its predictive capabilities. 7
check will be considered.
1. The Omnibus Tests of Model Coefficients. This is a test to check if the model with predictors
has improved compared to the model without predictors, i.e. rejecting or accepting the null
hypothesis. The model without predictors is basically the ratio between the two binary options.
The chi-square value 𝑋2 should be calculated (see equation) and compared to the critical value,
or alternatively the p-value should be computed from the chi-square value. The p-value is the
area under the chi-squared distribution of the domain from 𝑋2 to ∞. This distribution is a
function of the degrees of freedom. If the p-value is smaller than 0.05, the null hypothesis can
be rejected and the model with predictors is better than the model without. A disadvantage
with this check is that it does not tell how strong the model is.
19
𝑋2 = ∑(𝑂 − 𝐸)2
𝐸
Where 𝑋2 is the chi-square value, 𝑂 is the observed frequency and 𝐸 is the is the expected
frequency.
2. The next check will be looking at the 𝑟2 values. A 𝑟2 squared value indicates the variability of
the dependent variable, which is explained by the linear regression model. The 𝑟2 in classical
regression analysis is well known and often used as a measure of success of predictive
equations. For a logistic regression it is not possible to calculate the normal 𝑟2 value. Instead,
the pseudo 𝑟2 values can be calculated based on likelihoods of the regressed model and the
model without explanatory variables. Pseudo 𝑟2 values have some of the same properties as
classical 𝑟2 values, however the downside of using pseudo 𝑟2 values is that they are harder to
interpret. Nagelkerke came up with a 𝑟2 value which has the property that 0 < 𝑟2 < 1
(Nagelkerke, 1991).
3. Hosmer and Lemeshow Test for goodness of fit. This is another test to check how good the
model fits the data, where if the p-value is smaller than 0.05 it is defined to be not a good fit.
It should be noted that this p-value is different from the one described in point 1. This test is
very dependent on sample size and should not be interpreted without considering the sample
size. Hosmer and Lemeshow state that minimum sample size should be over 400 and is
especially powerful when the ratio of binary outcome is divided to a certain extent, for example
a 0.25/0.75 (Hosmer, Sturduvant, & Lemeshow, 2013).
4. It is possible to check to what extent the predictive capacity increased from the model without
predictors to the model with predictors.
If the model is found to fit the data and is of significant quality, the outcome can be interpreted.
5. Like stated before, the interpretation of the beta-coefficient or their odd ratios for a logistic
regression does not always give a correct insight into the relative importance of one predicting
variable. Usually only unstandardized beta-coefficients are calculated by statistical software. It
is possible to calculate the standardized beta-coefficients, so that the beta-weights can be
compared for importance in the model. The equation used for calculating standardized beta-
coefficients is:
𝛽𝑛,𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 = 1
(1 + 𝐸𝑋𝑃 (− (𝐿𝑁 (𝐴
1 − 𝐴) + 0.5 ∗ 𝐵 ∗ 𝐶)))⁄
− 1
(1 + 𝐸𝑋𝑃 (− (𝐿𝑁 (𝐴
1 − 𝐴) − 0.5 ∗ 𝐵 ∗ 𝐶)))⁄
Where 𝛽𝑛,𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 is the standardized beta-coefficients, 𝐴 is the mean predicted
probability for the dataset, 𝐵 is the unstandardized beta weight and 𝐶 is the the sample
standard deviation (King, 2007).
6. Each beta weight is tested by Wald statistics, which leads to an individual p level for each beta
weight. The Wald value is the beta-coefficient divided by the standard error to the power of 2.
The p-value is determined by use of the Wald distribution. Wald statistics are sensitive to
sample size.
7. The predicted discontinuity location vector can be compared to the original discontinuity
location vector. Possible mismatches between the two can be understood by looking back at
geological records and MWD graphs.
20
A final test can be done by using the regression equation to predict discontinuities in a dataset which is
not used as input to the regression.
2.7.5 Overview of statistical analyses
In Table 1, an overview of the advantages, disadvantages and data requirements for this thesis is given.
Table 1. Over view of statistical analyses.
Learning type Method Advantages Disadvantages Data requirement
Unsupervised PCA - Low noise sensitive - Reduction in data - Reduction of noise
- Restricted to linear correlations
- Sample adequacy, i.e. large enough sample size
K-means cluster analysis
- Fast for large datasets compared to other clustering methods - Simple and understandable use
- Different initial cluster positions could lead to different outcomes - Hard to determine the number of clusters - No appurtenance to several clusters
- An idea of how many clusters are could be present in the data
Supervised Multiple linear regression analysis
- Accounts for multiple predictive variables simultaneously
- Cannot tackle a categorical dependent variable - Sensitive to collinearity - Assumes linear relation ships
- Continuous or categorical independent variables - A dependent variable on continuous scale
Multiple logistic regression
- Accounts for multiple predictive variables simultaneously - Can tackle a categorical dependent variable, essential for this study - Does not assume linear relationships between dependent and independent variable
- Sensitive to collinearity
- Continuous or categorical independent variables - A dichotomous (binary) dependent variable
2.8 Correlation between MWD and geology
Several case studies have been done in Norway on the subject of MWD. In this section, the studies and
findings are summarized. At the end, the findings are listed.
2.8.1 Grouting volumes and MWD data
Høien and Nilsen have been looking at grouting volumes and MWD data of fracturing and water
conditions (Høien & Nilsen, 2014). The Løren tunnel is a 915 meter rock cover tunnel in an urban setting.
The main hazard was the lowering of the groundwater table which could cause settlements. Systematic
grouting and monitoring gave a good possibility to execute this research. The MWD-software used by
21
Høien and Nilsen is GPM+, delivered by Rockma AB, and data processing is based on Schunnesson’s
research. The water leakage mapping is done after the injection of grout, hence a quantitative analysis
of the relation between water leakage and MWD data is not possible. In this tunnel, all the grout holes
were first drilled and subsequently the grout was injected. In other cases, the grouting starts directly
after drilling any single grout hole. In this situation, the injected grout might influence the MWD data.
Høien and Nilsen found several sections that showed a good match or a visible relation between MWD
water conditions and fracturing with respect to the grouting volumes. For future projects, they
recommend separating the study in areas with and without groundwater.
2.8.2 Geological boundaries and zones of weaknesses
Hjelme worked on the same tunnel for his MSc thesis (Hjelme, 2010). The aim of his research was to
analyse the sensitivity of normalized MWD data, to identify geological boundaries and zones of
weakness of software in verifying the geology mapped in the tunnel and to investigate the correlation
between mechanical rock properties and MWD data. He used the software Tunnel Manager by Atlas
Copco AB which is based on Schunnesson’s research. Calibration of the drilling rigs is done by use of
data from other projects. Hjelme zoomed in on 5 chainages. He concluded that the validity of
normalization depends on the amount of data. The significant difference between the number of
injection holes and blast holes is reflected in the results. The higher number of blast holes resulted in
smoother lines after normalization than for injection holes, where the data set was 1/3 the size of the
blast holes. Another important point is the fact that at the beginning of each hole, drilling is executed
with a soft start, hence the first part of the data is deleted. The feed thrust highly affects the penetration
rate and torque, therefore normalization of penetration rate and torque pressure for feed is essential.
Hjelme found that there often is a mismatch between MWD data and geological mapping. Weakness
zones and transitions to other rock types are often found in both the geological records and MWD data
with a spatial difference of up to 2 meters. This occasionally high difference might be caused by use of
geological mapping done by engineering geologist on site. Occasionally features were only visible in the
MWD data or vice versa. It should be noted that the location of fractures and transitions in the
geological mapping are visualized in a spread out tunnel contour and that finding the exact locations of
features on this spread out contour is hard. Figure 9 demonstrates how Hjelme extracted the locations
of geological features from the geological records. He illustrated the investigated boreholes on the
geological mapping.
Figure 9. Illustration of how Hjelme placed boreholes in a spread out tunnel contour sheet for comparing MWD data to mapped geology.
22
2.8.3 Mechanical properties of rock and MWD data
Rødseth focused her research on the comparison between rock mechanical properties and MWD data
of the Eikremtunnelen, the Lørentunnelen and the Oppdølstranda tunnel (Rødseth, 2013). The
geological mapping is done by the geologist and engineering geologist of the Norwegian Public Roads
Administration. Rockma AB and SPSS are used to process the MWD data. The comparison of MWD data
and geological data is done in Microsoft Excel, where Rødseth made XY-plots to determine the
correlation coefficients (r2) between the MWD data and the fracturing number produced by Rockma,
RQD and RQD/Jn. In total Rødseth used about 4700 meters of tunnel length. Based on research, an
absolute correlation coefficient, I(r)I, higher than 0.5 represents a good correlation and an absolute
correlation coefficient lower than 0.3 represent a low correlation (Holmøy, 2008). Close to 2100 meters,
or 45 %, of tunnel MWD data showed a good correlation with the RQD, while only 16 % of the MWD
data showed a good correlation with RQD/Jn. Rødseth discussed the limitations of this research. The
geological records are often questionable. For example joint sets are documented as single joints with
no frequency. Further, Rødseth mentioned blast induced fractures and the difference between
resolution in geological data, RQD and MWD data.
Valli aimed his research on the hardness parameter at the ONKALO project in Finland [37]. He aimed to
correlate MWD hardness evaluations with the Schmidt hammer rebound number and point load tests.
Valli used the software Tunnel Manager MWD version 1.5 developed by Atlas Copco. This method of
processing MWD data has not been made public. The Schmidt hammer was used for approximately 126
meter of tunnel wall where every meter, about 8 measurements were made. 1385 point load tests
were used in the study. He found the correlation between MWD hardness and Schmidt rebound
number unsatisfactory. Changing Schmidt rebound numbers are found for not changing MWD
hardness. The results of the point load tests show a too small variation and is not usable for a
comparison with MWD data.
2.8.4 Mapped geology and MWD data
Herfindal based her research on the Skillingsmyrtunnelen (Herfindal, 2013). She investigated whether
there exists a correlation between MWD data, mapped geology, Q values and registered water inflow
by using the program GPM+ by Rockma. She found that the defined fracture outcome showed an as
high intensity in areas with both high and low RQD values. Hardness is found to correlate well with the
mapped geology since most of the dikes can be found back in the processed MWD data. Herfindal
recommends for future research to perform a more thorough geological field study or 3D surface
scanning in order to eliminate the difference in detail of geological documentation and MWD data.
2.8.5 Findings in research in the field of MWD technology
Based on the studied research, the following can be stated.
- The hardness parameter is found to correlate well with geological boundaries like dikes, zones
of weakness and rock type transitions. The maximum difference between location in MWD data
and geological record is found to be 2m.
- Several times, a good match or a visible relation between MWD water conditions and fracturing
with respect to the grouting volumes is found. The data is collected at a tunnel site with various
ground types like limestone, shale, sandstone, volcanic rocks and soil.
- RQD is found to correlate well with MWD fracturing number for 46% of studied tunnel sections
from 3 different tunnels. The relative block size, or RQD/Jn, did not show a convincing
correlation.
- Schmidt hammer hardness has not been shown to correlate with the MWD hardness number.
23
3. Data gathering and collected data
For this thesis two tunnel sites are visited (Figure 10) where different methods of gathering detailed
geological data are performed. All these methods have in common that the gathered data can be
compared to the MWD data of one or multiple holes. In this chapter the geology of the sites,
methodology and resulting datasets are discussed.
3.1 Preparation of fieldwork at Bjørnegårdtunnelen
3.1.1 Project
The Bjørnegårdtunnel is part of the Sandvika-Wøyen project for which the Norwegian Public Roads
Administration is the client. Traffic going through the city of Sandvika will be deviated by two 2-lane
tunnel tubes of 3.4 km long. Two thirds of the tunnel is constructed with drill and blast tunnels and one
third is constructed with the cut and cover method. Construction started in February 2015, drill and
blasting activities are planned to be finished in the autumn of 2016 and the project is planned to be
open for traffic in 2020.
3.1.2 Geology
The project is located in what is called the Oslo field. The rocks encountered in this project area are
deposited in the middle and late Ordovician in a sedimentary environment. The collision of Laurentia
and Baltica caused the rocks to fold on the outer borders of this collision zone, where the Oslo field was
located. In this period the Caledonian orogeny shaped the mountainous landscape in middle and west
Norway. In the late Carboniferous partial continental rifting opened a new sea which today is the Oslo
fjord. The sedimentary rocks are tilted towards the Oslo fjord. As the rifting continued batholith, dikes
and lava originated in the Oslo field (Ramberg, Bryhni, & Nøttvedt , 2013).
In Figure 11 two geological cross sections are given of the trajectory of both tunnel tubes. Drill and blast
is executed on the section from 300m to approximately 1650m northwest of the hill Hamang. A local
Figure 10. Map of southern Norway with tunnel site locations.
24
geological map is attached in appendix 1 (GEOVITA, 2014). The rocks encountered are limestone, shale
and intrusive rocks (see legend). One limestone formation consists of alternating layers with nodules.
In the engineering geological report the discontinuities predicted in the sedimentary rocks are mainly
bedding discontinuities with a steep dip and irregular oriented discontinuities. The spacing of bedding
discontinuities is predicted on a dm-scale.
Figure 11. Cross sections of geology encountered with constructing the two tunnel profiles (GEOVITA, 2014).
3.2 Preparation of fieldwork at Solbakktunnelen
3.2.1 Project
The Solbakktunnel is part of a project named Ryfast, which connects the inland and Stavanger and
improves the infrastructure on the east site of Stavanger. It consists of three tunnels. Solbakktunnelen
will be 14.3km long and 290m below sea level at its deepest.
3.2.2 Geology
The tunnel site is located in an area with Precambrian autochthonous basement, gneiss, and phyllite
form the Cambrian to Ordovician age (Multiconsult, 2009). The gneiss is found in two different groups,
Boknafjorddekket and Storheidekket (see Figure 12). The first group contains quartz and feldspar rich
gneiss which is locally banded. The second group contains mica rich gneiss, which is described as gneiss
with a grainsize ranging from fine to medium. The foliation found in outcrops has a spacing ranging
from 2 to 50cm. In addition there is a discontinuity set with a spacing varying between 0.5 and 1m. The
phyllite has been found in different intensities of metamorphism ranging from intense small scale
folding to foliation with a spacing of less than 10cm. As the contractor starts excavation at the east end
of the tunnel, they will first encounter gneiss from Storheidekket, then gneiss from Baknafjorddekket
and finally the phyllite. The exact location of the transition from gneiss to phyllite at tunnelling depth
cannot be predicted with high accuracy.
Malmøya Formation - Limestone, massive bedded and as lenses and nodules alternating with shales (80)
Skinnerbukta Formation - Shale, silty with thin calcareous zones (81)
Vik Formation and Rytteråker Formation - Limestone and shale, predominant limestone with nodules (82)
Solvik Formation - Shale with thin layers of siltstone and limestone (83)
Langøyene Formation - Massive limestone and shale layers (84)
Different formation - Shale with limestone nodules (85)
Fault
Dike
Measured dip
25
Figure 12. Geological map with Solbakktunnelen section, the yellow line is approximately 2km (Multiconsult, 2009).
3.3 Geological mapping
In this part, the possible geological mapping methods for this projects are discussed. Central with these
methods is the need for information of discontinuity type, discontinuity properties and location of the
discontinuity with an accuracy such that it can be compared to MWD data.
3.3.1 Tunnel wall mapping
A perfect blast has no overbreak and it creates straight planes between every contour hole. In such a
case, the borehole remains (half holes) can be used as a scanline where the location of a discontinuity
and corresponding geometrical properties of discontinuity are documented. In reality, a blast is never
perfect and borehole remains might be absent. The information wanted with this method are location
of the hole, overall RQD, Schmidt hammer strength of discontinuity planes, joint set, orientation,
aperture, persistence, infill, weathering state of discontinuity plane, joint roughness and joint alteration
number. In addition, the intact rock strength is wanted to evaluate a possible different between intact
rock strength and discontinuity plane strength, which might influence drilling parameters. Attention
should be payed to the difference between blast induced and natural fractures.
In case borehole remains are not present or out of reach, an area within reach can be mapped. The
same information should be gathered as with mapping borehole remains. The only difference is that
mapping becomes two dimensional which makes it harder to compare it with one dimensional MWD
data of a single borehole. A terrestrial laser scanner or photography would be useful to map
discontinuities, but were not used in this study.
In case large discontinuities are present in the face, these can be mapped with an estimation in a rolled
out tunnel profile, as described in section 2.5.
It should be emphasized that without the possibility to place discontinuities accurate in a 3D image or
along MWD data of a borehole remain, the data is not relevant for this study. This is expected to be the
main challenge during the fieldworks.
26
3.3.2 Borehole inspection
A method of capturing rock which is much less influenced by blasting is inspecting boreholes. This can
be done with different types of devices. The cheapest device to use is a regular inspection camera. It
should at least have proper lighting and depth indication. In addition, an indicator of the orientation of
the camera can be useful in case the camera might rotate. A disadvantage is that most inspection
cameras are aimed forward and the borehole wall is recorded with an angle less than 90°. An acoustic
or optical televiewer is a better alternative. An acoustic televiewer measures the change in wavelength
and amplitude of reflected soundwaves send towards the borehole wall. The borehole has to be filled
with water. The optical televiewer is a camera that records the reflection of the borehole wall a convex
mirror inside a tube. This gives a recording of the borehole wall with a 90° angle. The cylinder is pulled
out of the borehole with a speed of about 1m/min with a windlass, which registers the depth of the
device. Bright light is generated in the cylinder, which is kept central in the borehole. The optical
televiewer can be used in both dry and water filled holes, but the water needs to be clear. In Figure 13
an illustration is given (NGU, 2015). During the recording, the orientation of the televiewer is measured.
The outcome of a survey with a televiewer is a detailed digital image with a pixel size going down to
1mm2 of the borehole wall. This image can be processed with a simple program where three clicks on
a discontinuity lead to a sinusoidal approximation. The depth, orientation, self-given description of
discontinuity type, possible aperture and orientation statistics are the output.
3.4 Fieldwork at Bjørnegardtunnelen
During the first visit, 4 faces were under excavation. The engineering geologist was called to inspect the
geology of recent excavated rock several times a day. During these inspections, it was planned for this
thesis to document geology with more detail by mapping the tunnel wall. In addition, the Norwegian
Public Roads Administration and the contractor NCC agreed to drill 10 holes for borehole inspection.
During a second visit, an additional 15 holes were drilled. At the time of both fieldworks, limestone was
being excavated. An alternation of zones and layers with and without nodules caused that degree of
fracturing is not the only variable influencing MWD data. In this subchapter the fieldwork and resulting
data will be discussed.
Figure 13. Illustration of an optical televiewer (NGU, 2015).
27
3.4.1 Tunnel wall mapping and MWD data
In practise, mapping of the tunnel wall is a challenging task. The tunnel contour at the site was
supported with shotcrete almost immediately after the scaling and mapping. Stopping the drill and blast
cycle was not preferred. Issues like poor visibility, noise, reachability and most importantly, safety highly
influenced the ability to work. Several times the geological mapping was attempted without successful
mapping results. For constructional reasons a part of the northern wall of tunnel A was not covered
with shotcrete. There it was possible to spend more time and realize better conditions. In Figure 14, a
remain of an injection borehole is displayed. This borehole crossed 7 discontinuities. The white colour
is the injected grout. The exact location of the end or beginning of the borehole is unknown, but the
fracture studied by the person in the picture is a fracture with an aperture of 3 to 4 cm filled with clay.
This fracture and its approximate location, 0.3m before profile 955, will most likely be visible in MWD
data with mapped data and is therefore chosen as the start point of observations. In Table 2 the
observed discontinuity and geometrical properties are given.
Table 2. Geometrical properties of discontinuities crossing an injection borehole remain close to profile 955 in tunnel A.
Fracture number 1 2 3 4 5 6 7
Distance (m) 0 -0.45 -1.95 -2.60 -3.25 -3.34 -4.41
Location (m) 954.7 954.25 952.75 952.1 951.45 951.36 950.29
Dip direction (°) 160 155 160 80 150 150 152
Dip direction relative to tunnel (°)
210 205 210 130 200 200 202
Dip angle (°) 74 70 70 70 70 70 70
Aperture (mm) 30-40 10 0 0 0 0 0
Persistence (m) Whole contour
Whole contour
Whole contour
Whole contour
4 4 Whole contour
Infill Clay Calcite - - - - -
Weathering None None None None None None None
Figure 14. Mapping at tunnel wall in tunnel A at profile 955 of an injection borehole remain. Person length is 1.75m.
28
A few times, there was a possibility to have a closer look at the tunnel contour from a platform. In the
section 510 to 505 in tunnel tube A, a calcite vein persisting along the whole tunnel contour is
documented. The discontinuity has an orientation of 155/87 relative to north and an aperture of 5 to
10 mm. The bedding is approximately orientated 200/80 relative to north. In Figure 15 you see a graph
which illustrates the continuation of the discontinuity over the contour like explained in section 2.5.
Figure 15. Graphical representation of calcite infilled discontinuity in tunnel A at profile 505.
During grout injection, there was plenty of light and time to map the tunnel front. A disadvantage is
that the walls are covered with shotcrete and discontinuities visible in the face need to be extrapolated
to an estimated point of intersection with a borehole. Extrapolation of discontinuities is a large possible
error since the straightness of a fault plane is hard to predict. When discontinuities are close to parallel
to the tunnel face, extrapolating faults planes can be based on multiple orientation measurements.
Figure 17 shows tunnel face 970 in tunnel A where several large discontinuities are close to parallel
oriented with the tunnel face. Infill differs from calcite, unknown, syenite (most likely) and no infill.
Apertures vary from 0 to 30mm. In Figure 16 all the information from both contour mapping and face
mapping can be found. The locations of the discontinuities is estimated on site.
505.00
510.00
-14.00-7.000.007.0014.00
Discontinuity in tunnel A section 510-505
Fracture 1, calcite infill of 5 to 10 mm, 160/87
965.00
970.00
975.00
-12.00 -6.00 0.00 6.00 12.00
Fractures in tunnel section 965-970
Fracture 1, red infill of 30 mm or moreFracture 2, no infill observedFracture 3, red infill of 20-30 mmFracture 4, no infillFracture 6, unknown infill of 20 mm in lower 5 meters, fracture not visible higher then 5 metersFracture 7, no infill or 1 mm of calciteFracture 8, 5-22 mm calcite infillFracture 9, 10-30 mm calcite
Figure 16. Graphical approximation of positions of discontinuities seen along tunnel face 975 in tunnel tube A.
29
Figure 17. View along the tunnel face 975 tunnel A. The packers are separated by approximately 1.2m (top left), the packers stick approximately 30 to 40 cm out of the wall (top right), the rock bolts in the shotcrete have a diameter of 20cm (lower right).
30
3.4.2 Borehole inspection and MWD data
Discontinuity mapping at the tunnel face has the disadvantage that it is hard to define the exact location
of a discontinuity with regards to a borehole. A televiewer is possibly the best method to gather data
for a comparison between geology and drilling parameters. The Geological Survey of Norway provided
the televiewer service.
During the first field work, the possibility was given to drill 10 holes of 5 meters long. The holes had to
be drilled in reach of electricity and could not penetrate the grout screen. Drilling of holes also had to
fit in the drill and blast activities of the contractor NCC. Moving the drilling jumbo for each hole would
take too much time so it was chosen to park the drilling rig at three places and to drill multiple holes
with different orientations at every location. The drilling arm of the AMW drilling jumbo had a forward
reach of about 4m. The blasthole size used at the Sandvika site is 48mm. Due to the size of the
televiewer all holes had to be enlarged, once with the normal blasthole drill bit and once with a large
hole drill bit to reach a 110mm diameter. In order to receive both MWD data from the normal hole
drilling and large hole drilling, drilling settings had to be adjusted on the drilling jumbo. Sampling
interval was reduced to 10mm and the drilling parameters for torque pressure, feed pressure and
percussive pressure for the large hole drilling had to be set the same as the normal hole drilling. One
test hole was drilled with these settings during drilling of large holes in a face. During the second
fieldwork, another drilling jumbo was available. Unfortunately no other rock type was encountered in
the meantime, hence holes were drilled in limestone again.
The most important aim of this part of the fieldwork is to see how drilling parameters behave when
drilling through discontinuities. Therefore, it was essential to encounter discontinuities. To analyse the
response only of discontinuities, the other rock mass properties should be constant. At this site the
lithological discontinuities, or bedding, are dominant. Open fractures and discontinuities with infill were
less dominant. Documented geology by engineering geologist on site, like described in section 2.5, was
used to determine locations to drill holes. Since bedding would most likely be encountered in the holes,
it was chosen to focus on trying to capture other discontinuities than bedding. In addition, it is preferred
that the rock mass changes as little as possible. The locations chosen are displayed in figures given
below . The stars indicate the approximate location of drilling. The drilling direction is opposite to the
profile numbering with angles of 55° or 90° with respect to the tunnel axis. Twice, an angle of 20° has
been tried, but the operator had problems finding grip and when drilling large holes there were
problems with keeping the drilling arm aligned with the pilot hole. This lead to the loss of a large drilling
bit and a drilling string. The drilling bits which were used where all relatively new, or brand new after
losing a drilling bit.
The location of borehole 1 and 2 was chosen since the observation by the engineering geologist
described an infill of clay for all 5 discontinuities. One discontinuity was observed with an aperture of
1cm. At this location, the RQD is estimated to be 62.5 % and two joint set plus random joints were
observed. See Figure 18.
Figure 18. Approximate location holes 1 and 2 drilled with angles of 20° to 30° from the tunnel axis.
31
At the location of borehole 3 to 10, three fractures with an aperture of 1cm, 30cm and 40 cm are
documented. It could well be that the units have gotten mixed up and that the aperture is 3cm and
4cm, as the discontinuities are mapped by different engineering geologists. The discontinuity with an
aperture of 1cm has a clay infill. At this location, the RQD has been documented between 62.5 % and
85 % and two joint sets plus random joints have been observed. See Figure 19.
Boreholes 11 to 15 are located where discontinuities were observed by the author during the first
fieldwork. Even though the engineering geologist did not document all the discontinuities present,
multiple discontinuities with different types of infills are present at that location. It should be noted
that the documentation is done by a substitute for the normal engineering geologists on site. In Figure
20 in the previous section, the discontinuities are mapped. During planning of drilling, the locations are
studied for feasibility and navigation. At this location, a small part of the shotcrete collapsed and the
rock wall is visible. Even though the rock was covered in dust and dirt, closed discontinuities and
discontinuities with infills of 1cm syenite and 3cm clayey content are observed.
Boreholes 16 to 20 are chosen at a location where multiple discontinuities are observed. One
discontinuity has a 4cm infill of clay. RQD is documented to vary between 62.5 and 70 % and two joint
sets plus random joints are observed. See Figure 20.
Figure 19. Approximate location holes 3 to 10 drilled with angles of 55° and 90° from the tunnel axis.
Figure 20. Approximate location holes 11 to 15 close to 975 and holes 16 to 20 close to 960, drilled with angles of 40° to 60° from the tunnel axis.
32
The location of the remaining boreholes 21 to 25 is chosen based on the documentation of 5
discontinuities of which three have an aperture of 4cm. The documented RQD is 62.5 % and one joint
set plus random joints have been observed. See Figure 21.
The data set of the televiewer borehole inspection consist of 2 borehole recordings of 4m length and
23 borehole recordings close to 5 meter length. For an unknown reason, the adjustments in sampling
interval in the drilling system did not lead to a decrease of sampling interval in the MWD data. It could
be that the minimum sampling distance that can be registered by the drilling systems is 0.02m. The
2718 samples in normal hole drilling with the Atlas Copco jumbo have an average interval of 0.0271m
with a standard deviation of 0.010m. For the large holes these numbers are: 3249 samples, average
interval of 0.0225m and a standard deviation of 0.0046m. The 2430 samples in normal hole drilling and
2283 samples in large hole drilling with the AMW drilling jumbo have an average interval of 0.0200m
and standard deviation of 0.0005m. Another difference between the two drilling rigs observed in the
MWD data is that overall the hammer pressure of the AMW jumbo is fluctuating around 140 bar, while
for the Atlas Copco jumbo, it fluctuates around 200 bar. Also for the rotation pressure, water pressure
and water flow there are overall differences of 60 bar, 5 bar and 40l/min.
After drilling of every pilot hole, the drilling boom needed to be moved in order to save and receive
data from both the pilot hole and the large hole. This caused problems with finding the exact same
drilling orientation of the pilot holes for the large holes. When a large hole drill bit is not aligned with a
pilot hole, it causes the drill bit to jam and asks for readjustments by the operator. In Figure 22, the
difference between the drilling log of a pilot hole and the corresponding large hole is illustrated with
feed pressure from hole 9. At about 3.15m, 4.6m and 4.9m the feeder pressure from the large holes
drop due to the process described above. During drilling, it was written down when the drill bit got
jammed. This happened mainly with drilling the large holes. One additional event seen in all MWD data
is the slow start. The operator controls the moment when drilling in full speed is started. Drilling with
full speed from the beginning is never done. In Figure 22, the feeder pressure is low in the beginning
and increases after about 0.6 meter for the normal hole and after about 1.5m for the large hole. This
effect should be disregarded when analysing MWD data. In addition, hole 1 showed an unexplainable
low penetration rate.
The televiewer measurement of borehole 1 is defined for a depth from 0.0m, the bottom, to -4m. For
borehole two, the bottom is set to -1.84m. The remaining holes have a reference point of 5m. The
televiewer had to be held in place by hand the last 0.5m of each measurement. This caused poor
recordings for the last 0.5m. While drilling borehole 17, the drilling bit broke at a depth of about 4.3m
deep. Drilling the large hole did not go smooth and the drilling bit had to be pulled out of the hole half
way. At the point where the drilling bit enters the hole again, a new drilling log file is made. The starting
depth of in the new log file can be anywhere between the tunnel wall and the depth where drilling
stopped before the drilling bit was pulled out. This led to a high uncertainty in depth of the large hole
since two log files have to be merged.
Figure 21. Approximate location holes 21 to 25 drilled with angles of 20° to 30° from the tunnel axis.
33
In the recordings, lithological banding, calcite veins, fractures with soft infill and fractures without in fill
are present. Lithological banding is dominant and present in all televiewer measurements. Veins are
present in all holes except holes 10, 24 and 25. They are most likely filled with calcite, which was the
only vein infill seen in fresh rock. Borehole 1 and 2 do not show the expected fractures with a clay infill.
The clay might be flushed out during drilling. In borehole 5 and 6, the mapped fractures are visible.
Here, the black bands are zones where light is not reflected to the televiewer. At the location of
boreholes 11 to 15, a fracture with soft, fine grained, dark red infill is present but not detected in the
televiewer measurements. Other fractures are detected at this location. Boreholes 11, 12 and 13 are
drilled partly in a red sandstone. In holes 21 to 25, fractures with a softer, more clayey, green infill are
captured. A sample is taken from a fracture in crossing the first 10cm of a borehole. From studying the
tunnel contour, it is know that all discontinuities except for calcite veins have zero or nearly no tensile
strength. This means that all discontinuities except for calcite veins would contribute to the calculation
of RQD.
The depth registration of the reel, which pulls the televiewer out of the holes is accurate. There was
however a problem with controlling the movement of the cable when the reel was placed multiple
meters from the hole. When recording holes 21 to 25, the minivan containing the reel had to be parked
about 8m from the tunnel wall. This made it hard to control the movement of the cable. Wobbling
might have led to an inaccurate depth registration.
The image processing is done with the software RGLdip by Robertson Geologging. The outcome of this
is a table with the upper and lower depth of sinusoidal approximation in mm accuracy relative to the
reference point, the average depths in mm accuracy relative to the reference point, orientation relative
to north, aperture and a self-interpreted discontinuities definition for each discontinuity. In appendix
2, the 25 recordings and these outcomes are given. In appendix 3, the MWD data is given.
0
20
40
60
80
100
0 1 2 3 4 5Feed
er p
ress
ure
(b
ar)
Depth (m)
Feeder pressure
Normal
Large
Figure 22. Graph showing drops in feeder pressure due to jamming of the drill bit caused by difficulties with aligning drilling direction of the large hole.
34
3.4.3 Overview of collected data
The geological data and MWD data collected at Bjørnegårdtunnelen is summarized in Table 3. Only the
usable data for this study is discussed in the report.
Table 3. Overview of collected data at the Bjørnegårdtunnelen.
Location Geological data MWD data Correlation type
Tunnel tube A Close to profile no. 955
Borehole remain of about 5m long crossing 7 discontinuities of which one with clay infill, one with calcite infill
MWD data with a sampling interval of 2.48cm and standard deviation of 0.44cm.
Visual comparison
Tunnel tube A Profile no. 510 to 505
Position and orientation of a calcite vein persisting along whole tunnel contour
3D images of parameters Visual comparison
Tunnel tube A Tunnel face at profile no. 970
Positions and orientations of 9 fractures persisting along the whole face. Infill is varying from clayey, reddish unknown, unknown and none.
3D images of parameters Visual comparison
Tunnel tube A Close to profile no. 700
2 borehole images with calcite veins and lithological discontinuities
MWD data with a sampling interval for normal and large hole size of 2.00cm and standard deviation of 0.005cm. Rotation speed is missing.
Visual and statistical comparison
Tunnel tube A Close to profile no. 600
8 borehole images with calcite veins, lithological discontinuities and fractures without infill
Tunnel tube A Close to profile no. 930
5 borehole images with calcite veins, lithological discontinuities and fractures without infill
MWD data with a sampling interval for normal hole data of 2.71cm and standard deviation of 1.00cm. Large hole data sampling interval average 2.25cm and standard deviation 0.46cm.
Tunnel tube A Close to profile no. 975 and 960
10 borehole images with calcite veins, lithological discontinuities, fractures with softer clayey infill and fractures without infill
3.5 Fieldwork at Solbakktunnelen
At the time of excavation, 2 faces were under excavation. At this site the work continued day and night
and several times a day there was the opportunity to map the geology of recent excavated rock. On
site, there was a borehole camera present, which could be used to record large hole drillings in the
tunnel face.
3.5.1 Tunnel wall mapping and MWD data
During the whole fieldwork, the rock excavated was highly foliated phyllite and larger discontinuities
were absent. Often rock fragments were falling after scaling and it was not safe to stand under recently
excavated rock that had not been supported. This made mapping at the tunnel face for the purposes
for this thesis impossible.
35
3.5.2 Borehole inspection and MWD data
The inspection camera was used at 3 different tunnel faces to inspect the large holes. The camera used
is an Inspector ® 48 produced by CamTronics with a resolution of 720x576 pixels. Unlike the televiewer,
this inspection camera produces a regular video of the borehole wall. The equipment includes a
portable reel and computer on which real life footages was displayed (see Figure 23. The inspection
camera and portable computer.). It was unfortunately not possible to spend more time besides making
one recording for each hole. The inspection camera viewed forward with an 84° angle and bright
artificial light. On the reel, a depth indicator with an accuracy of 0.1m was mounted. It was difficult to
keep the cable tensioned between the reel and entrance of the holes. The depth measurements are
not accurate. The differences in actual drilling depth and recorded depth with the reel go up to 2.1m.
Most differences are within 1m. The image is automatically horizon corrected. After the first time of
using the camera, it turned out that the video footages was blurry when moving. After this, it was tried
to hold the camera still in the hole for a second every few centimetre. On first sight, all holes show the
similar highly foliated phyllite.
Discontinuity are difficult to identify or are absent in the filmed holes. In Figure 24, screenshots of
typical video footages is given. Of all holes, MWD data of both small and large hole is available (appendix
4). The sampling interval is 20mm. Water pressure, water flow and rotational speed are missing due to
an unknown reason. The length of the holes varies between 4m and 5.7m. Holes 10, 11 and 12 are 4m
holes. These are drilled in a tunnel tube where the blasting length was reduced due to poor rock quality
and short stand-up time of unsupported rock. Hole 5 is a short hole as well. Hole 4 and 5 cross each
other at a depth 4 meters. This might indicate that the operator stopped drilling hole 5 at the depth
where the hole met hole 4.
Figure 23. The inspection camera and portable computer.
36
3.5.3 Overview of collected data
The geological data and MWD data collected at Solbakktunnelen is summarized in Table 4. Only the
usable data for this study is discussed in the report.
Table 4. Overview of collected data at the Solbakktunnelen.
Location Geological data MWD data Correlation type
Tunnel tube A Profile no. 4184 Tunnel tube B Profile no. 4276 and no. 4281
11 borehole videos capturing highly foliated phyllite, no large discontinuities visible
MWD data with a sampling interval of 2.00cm and standard deviation of 0.005cm.
Visual comparison
Figure 24. Screenshots of typical video footages.
37
4. Visual validity assessment of MWD data
In this chapter, the geological data and MWD data will be compared visually. The geological data gained
by mapping the tunnel contour will be analysed first, followed by the data collected with borehole
inspection.
4.1 Tunnel wall mapping data
The raw and processed MWD data is given in appendix 5. The processed parameters are given for
computations of a moving average of 4, 6, 8 and 10 values. In all graphs, the orange lines represent the
estimated location of the discontinuities. The discontinuities have dip directions of 70° or larger relative
to the drilling direction. The average sampling distance is 25mm with a standard deviation of 4mm.
By observing the location of fracture 1 at 954.7 in appendix 5, it can be concluded that the correct MWD
data is found. This fracture has an opening of 30-40mm with a clay infill. The operator experiences this
kind of fractures as a drop in penetration rate since the drilling settings are adjusted automatically in
such a case. The penetration rate shows the exact same behaviour, 7 cm from the observed location.
This fracture would be visible in neighbouring holes since all holes have a slightly different orientation
and the fracture is not perpendicular to the tunnelling direction. For this specific fracture, the hammer
pressure, feeder pressure and rotation pressure drop significantly. Apart from discontinuities 1, 5 and
6, there seems to be no distinctive response to the discontinuities in the MWD data. Discontinuities 5
and 6 are separated by 90mm and there seems to be one response in penetration rate and rotation
pressure.
In appendix 5, fracture 1 is clearly visible in the normalised penetration, RMS normalized penetration
and RMS rotation pressure. When a moving average out of 4 measurements is used, the response
represents the fractures aperture better. Fractures 5 and 6 remarkably result in a drop in normalized
penetration and rotation pressure. Again, the moving average of 4 measurements represents the
spacing of the fractures 5 and 6 best in the RMS normalized penetration rate. Fractures 2 and 3 seem
not to influence MWD data in any way and fractures 4 and 7 could have influenced the MWD data.
The exact position of the fracture observed in profile 510 to 505 is inaccurate. For that reason, it is
chosen to see if this fracture can be seen in 3D images of Bever Team. All parameters are inspected
with different colour settings and moving averages. The fracture with calcite infill of 5-10mm and an
orientation of 155/87 is not visible. In appendix 6, the RMS normalized penetration for a moving
average of 4, 6 and 8 measurements is given. In these images, several trends of darker colours can be
identified running diagonally through the profile. These trends are in line with the orientation of the
bedding discontinuities.
In Figure 25, the RMS normalised penetration with a moving average of 4 values of the contour of
profile 965-975 is given. It is difficult to identify discontinuities in images like these. In appendix 7, more
images are given of various parameters and moving averages, which all together led to the
interpretation in the right part of Figure 25. The lines may indicate the positions of the observed
discontinuities described in section 3.4.1. A moving average of 4 values of RMS normalized penetration
gives the clearest representation. The increase in penetration rate is not measured and calculated in all
holes on the interpreted location of the discontinuities.
In Figure 26, an image of the RMS normalised penetration with a moving average of 4 values of the
floor of profile 965-975 is given. The discontinuities are mapped 1.5 to 2m above the floor, much closer
than to the crown of the tunnel. In Figure 26, the discontinuities are better visible and in the right part
38
of the figure, the lines indicate possible positions of the observed discontinuities. Again, a moving
average of 4 values gives the best representation of the fractures, in appendix 7, images of other
parameters can be found. It can be observed that the increase in penetration rate is not present in all
holes along a fracture plane.
Actual blasting of profile 965-975 caused underbreak and dangerous hanging blocks. The round needed
to be re-blasted 3 times before getting the wanted contour shape. This might well be due to the
presence of these fractures.
Reasons for why an increase in normalized penetration is not seen in all holes on the discontinuity
intersections might be the varying aperture or the sampling interval, which is 29.1 mm on average with
a standard deviation of 7.7mm for all holes displayed in the figures.
Fracture 1,2 or 3
Fracture 4,6 or 7
Fracture 8
Figure 25. Top view of RMS normalised penetration with a moving average of 4 values of the contour showing possible locations of mapped discontinuities.
Fracture 1, 2
or 3
Fracture 4, 5
or 6
Fracture 8
Figure 26. View from underneath the tunnel, or the tunnel floor, of RMS normalised penetration with a moving average of 4 values showing possible locations of mapped discontinuities.
39
4.1.1 Overview of observations in MWD data and mapped geology
In Table 5 an overview is given of the discontinuities observed in MWD data.
Table 5. Overview of observations in MWD data and mapped geology.
Location Geological data Discontinuities observed in MWD data.
Tunnel tube A Close to profile no. 955
Borehole remain of about 5m long crossing 7 discontinuities of which one had clay infill (30-40mm) and one had calcite infill
- Clay infilled fracture caused a clear drop in raw and processed MWD parameters - 2 closely spaced fractures gave the expected response in penetration rate and rotation pressure - All observations are clearest in processed parameters with a moving average of 4 values
Tunnel tube A Profile no. 510 to 505
Position and orientation of a calcite vein persisting along the whole tunnel contour
- Discontinuity not visible in MWD data
Tunnel tube A Tunnel face at profile no. 970
Positions and orientations of 9 fractures persisting along the whole face. Infill varies from clayey, reddish unknown, unknown and none.
- Looking at 3D images of the contour, 3 fractures are identified - Looking at 3D images of the floor, 4 fractures are identified - Observations are clearest in RMS normalized penetration with a moving average of 4 values - Unknown which fractures out of the 9 are registered by MWD data
4.2 Televiewer data
4.2.1 Data preparation
Due to the geometry of the televiewer, presence of drilling cuttings in the borehole and other
conditions, it is inevitable that MWD depth and televiewer depth are not aligned. These two depths
have to be adjusted manually. It is chosen to shift the MWD depth since the televiewer depth is fixed
on images which cannot be changed. By looking for and comparing depths of markers, like for example
a large fracture or start of a zone with discontinuities, and corresponding responses of parameters of
the raw MWD data graphs at the approximate same depth, the depth of MWD data is adjusted. The
quality of this shifting highly depends on the presence of a good marker. This process is done by both
looking at raw data and MWD graphs.
In the next 12 subsections, the applied shifts are discussed. Boreholes 1 to 10 have been drilled with an
AMW drilling jumbo. After a first quick inspection, it seems that shifts will be in the order of a couple of
centimetres. The televiewer recordings of holes 3 to 10 are between 4.5m and 4.7m long. The last few
cm were not recorded. The televiewer results of hole 1 and 2 have a different starting point and
additional shifting is needed.
Boreholes 11 to 25 are drilled with an Atlas Copco drilling jumbo. Most televiewer recordings are close
to 4.6m long. Often the whole borehole is filmed and remarkably, the end of the borehole is visible at
this depth of 4.6. The recording of shotcrete or open air at a depth of 4.6 could imply either that the
40
holes are not 5m long or that the recording did not begin. It is assumed that the boreholes are not 5m
long for several reasons even though MWD data implies this. The first reason is that it is unlikely that
the televiewer did not get all the way to the bottom of the holes. Holes were drilled a few degrees up
and therefor flushing successfully removed all the drilling cuttings left. Secondly, the televiewer was
pushed in the hole and the televiewer always stopped abruptly. The third and most important reason
is that presumably the difficulties to find grip between the drilling bit and tunnel wall lead to premature
registration of MWD data. It is observed in several holes that the first 20cm shows strong peaks and
dips which could reflect scraping along the tunnel wall. This matter might lead to larger shifts than holes
1 to 10.
The visual representation of the televiewer and the corresponding MWD graphs can be found in
appendix 2 and 3. The raw data and the processed data for 4 different moving averages of 2, 4, 6 and
8 values are plotted against the shifted depths. For regular use of MWD data, usually a moving average
under 10 values is used.
4.2.2 Borehole 1
The reference point for the televiewer data is set at 0.0m for the bottom of the borehole. For this
reason, the MWD data is shifted with -5.0m. As a marker, the discontinuity with an upper depth of
approximately -2.43m and a lower depth of -1.744 is chosen. It seems that the penetration rate of the
normal hole is influenced by the presence of this fracture. Where penetration rate is relatively stable
before this discontinuity, it is increasing and fluctuating from this fracture on to the end of the hole.
With this discontinuity as a marker, the MWD data of the normal hole is shifted -5.09m, and it assumed
to be a reasonable shift.
The MWD data of the large hole does not show this behaviour at the marker. When applying the same
shift of 5.09m, there seems to be a close correlation between the dip in penetration rate of the large
hole around -0.7 and a discontinuities interpreted aperture. If the dip in penetration rate is chosen as
a marker, the MWD data of the large hole should be shifted -5.1m for a good alignment. With this shift,
both penetration rates show a dip at -3.42 for a reason not seen in geology. With the discontinuity as a
marker and the corresponding dip at -3.42m, the MWD data of the large hole is shifted -5.1m, and it is
assumed to be a reasonable shift.
It can clearly be seen that penetration rate, hammer pressure, feeder pressure, normalized penetration
rate and RMS normalized penetration rate of the normal hole increases from the first fracture to the
bottom of the borehole, whether if there is a discontinuity or not. The penetration rate shows a slightly
larger dip when encountering the fracture between -0.8 and – 0.55. The water flow is reducing only at
the fracture between -2.4 and -1.6.
The MWD data of the large hole does not seem to be influenced except from the dip in penetration
rate, hammer pressure, feeder pressure, rotation pressure. The fracture between -0.8 and – 0.55 is
best represented by the normalized rotation pressure and RMS normalized rotation pressure with the
higher moving average values.
4.2.3 Borehole 2
The reference point for the televiewer data is set at -1.84m for the bottom of the borehole. For this
reason, the MWD data is shifted with -3.16m. For this hole there is no clear marker reflected in the
MWD data of the normal hole. It is assumed that the peak around -0.2m is due to the calcite vein
between -3.2m and -1.8m. With this shift of -3.29m, there is a dip aligned with the interpreted aperture
41
at -0.2m. With these two markers, the MWD data of the normal hole is shifted -3.29m and it assumed
to be a poor shift.
Except for the penetration rate, the MWD data of the large hole shows a decrease between 0.2m and
0.4m. It is assumed that this is due to the calcite vein between 0.6m and 0.4m. With this as a marker,
the MWD data of the large hole is shifted -3.03m, and it assumed to be a poor shift. With this shift the
dips in penetration rate at 1.8m are perfectly aligned.
During drilling of the two holes, difficulties where encountered with finding the correct drilling direction
for the large holes. The difference between the two shifts is 26 cm and could be caused by these
difficulties.
The MWD data of the normal hole shows a saw tooth behaviour in penetration rate, hammer pressure
and feeder pressure for a reason not explained by geology. It is unsure if the peak at -2.1 is part of this
behaviour or if it reflects the presence the calcite vein between -3.2m and -1.8m. The large calcite vein
between 0.6m and 0.4m is not reflected in its totality. There is a sharp dip at 0.2m.
The MWD data of the large hole is much more constant and represents the geology much better than
the large hole. The large calcite vein between -3.2m and -1.8m can be seen back in feeder pressure,
hammer pressure, rotation pressure and processed data.
4.2.4 Borehole 3
For borehole 3, it is hard to find solid markers. It is chosen to shift -4cm, so that there is a small dip at
2.17m is aligned with the vein between 2.0m and 2.28m and so that the small peak is aligned with the
fracture at 3.8m. With these two markers, the MWD data of the normal hole is shifted -0.04m, and it
assumed to be a fair shift.
The MWD data of the large hole is not shifted. There is reflection of a marker in MWD data to determine
the correct depth of the MWD data.
The two markers used for shifting the MWD data of the normal hole are the only two discontinuities
reflected in raw and processed penetration rate. However, these observations are questionable since
the magnitude of the responses does not differ much from the rest of the measurements. The
lithological banding is not reflected consistently.
The penetration rate and normalized penetration show intense fluctuation on a small scale for most of
the length of the borehole. Between 0.8m and 1.2m fluctuation is much less compared to other depths
with lithological discontinuities. It is unclear if this absence or presence of discontinuities leads to a
more fluctuation response.
4.2.5 Borehole 4
As for borehole 3, it is hard to find solid markers in borehole 4. The same calcite veins as in borehole 3
are visible. It is chosen to shift -16cm, so that the small dip between 1.8m and 1.9m is aligned with the
veins between 1.7m and 2.0m. With this marker, the MWD data of the normal hole is shifted -0.16m,
and it assumed to be a shift of fair reliability.
The MWD data of the large hole is not shifted. There is no reflection of a marker in MWD data to
determine the correct depth of the MWD data.
The veins between 1.7m and 2.0m seem to correlate with the behaviour of raw and normalized
penetration. For the discontinuities, there is no clear response in MWD data detected.
42
Like for borehole 3, is unclear if the intense fluctuation in penetration rate and normalized penetration
rate is cause by lithological discontinuities.
4.2.6 Borehole 5
The larger fracture between 3.49m and 3.62m can clearly be seen in most raw MWD parameters of
both the normal and large hole. With this marker, the MWD data of the normal hole and large hole is
shifted respectively -0.102m and 0.06m. Both are assumed to be a perfect shift.
There is a clear response for the fracture between 3.49m and 3.62m, namely the penetration rate
increases, the hammer pressure drops, the feeder pressure drops, the processed penetration increases,
the RMS normalized rotation pressure increases for both normal and large hole MWD data. The water
flow and processed water are only influenced by this fracture while drilling the normal hole. The
rotation pressure and processed rotation pressure are only influenced by this fracture while drilling the
large hole. The response in MWD data for the fracture between 4.57m and 4.65m is less clear. It is
shifted forward or sometimes absent, but the response for this fracture is similar in direction. There is
a clear difference in response between the depths of these two factures and the rest of the borehole.
The lithological discontinuities do not show a sharp fluctuation but over the larger area, the penetration
rate and normalized penetration rate of the normal hole seem to be higher and more fluctuating. This
might be due to a small difference in rock properties between the uniformly coloured grey parts and
the non-uniformly coloured grey part.
4.2.7 Borehole 6
Similar to borehole 5, the large fracture between 3.3m and 3.42m is used to determine the shift for
both normal and large hole. With this marker, the MWD data of the normal hole and large hole is shifted
respectively -0.16m and 0.02m. Both are assumed to be a perfect shift.
The larger fracture between 3.3m and 3.42m is the same fracture as the fracture in borehole 5
(between 3.49m and 3.62m). The response in MWD data for the fracture around 4.4m is similar for the
fracture in borehole 5 (between 4.57m and 4.65m). It looks like the two calcite veins around 4.27m and
4.35m are included in the response of the fracture around 4.4m. There is a clear difference in response
between the depths of these two factures and the rest of the borehole.
Like for borehole 5, the lithological discontinuities do not show a sharp fluctuation. The response over
a larger area is clearer for borehole 6 than for borehole 5. Here, the difference between rock properties
might also be the cause for the difference in response.
4.2.8 Borehole 7
The calcite vein in between 2.7m and 3.12m does not seem to give a response in MWD data. The only
option to shift the MWD data is by looking at the dip around 1.4m in penetration rate. This dip is also
visible in borehole 8 at more or less the same spot. It is possible that this is related to the part of
limestone that is uniformly coloured grey (between 1.3m and 1.47m), which like in borehole 5 and 6
might cause the possible low penetration rate. If the borehole is shifted with this marker, it looks like
the first two lithological discontinuities give an expected response in penetration rate. With this marker,
the MWD data of the normal hole is shifted 0.159m, and it assumed to be shift with fair reliability.
The MWD data of the large hole is not shifted. There is reflection of a marker in MWD data to determine
the correct depth of the MWD data
There seem to be no relation between the discontinuities and any of the MWD parameters.
43
4.2.9 Borehole 8
Like mentioned before, the situation for borehole 7 and 8 is similar. If the uniformly coloured grey part
is chosen as a marker, the shift for the MWD data of the normal hole is 0.11m. For borehole 8, the large
hole MWD data seems to give the same response. For that reason, the MWD data of the large hole is
shifted with -0.03m. These shifts are classified to have a fair reliability.
There seem to be no relation between the discontinuities and any of the MWD parameters. The data
shows intense fluctuation, but this cannot be correlated to specific discontinuities.
4.2.9 Borehole 9 and 10
Unfortunately, there are no useful markers to connect the MWD data to the televiewer measurements.
No shift is applied. There is no clear relation between discontinuities and MWD data or sections without
discontinuities and MWD data.
4.2.10 Borehole 11 to 15
One fracture is visible in boreholes 12 to 15 at the depths 4.4m, 3.6m, 2.5m and 2.4m. The response in
penetration rate is a small peak in normal and large hole data. With this fracture as a marker, the MWD
data of normal sized holes of borehole 12 to 15 is shifted respectively 0.25m, 0.2m, 0.22m and 0.3m.
For the large holes these shift are 0.12m, 0.17, 0.06m and 0.21m. These shifts are assumed to be
perfect. Borehole 11 does not include this fracture. When shifting the normal hole data with 0.27m,
the penetration rate shows small peaks close to 3.3m and 3.85. On these depths a calcite vein and
possible fracture are located. This shift is assumed to be of fair reliability. The large hole data does not
show responses to these fractures in penetration rate. Instead, the rotation pressure seems to be
influenced by these fractures. With this shift of 0.27m, it is assumed to be a fair shift.
As mentioned in chapter 3.4.2., this location was chosen since the auteur had observed several
discontinuities at there. When studying boreholes 11 to 15, at least the discontinuity types calcite veins,
bedding discontinuities, an open fracture and possibly closed fractures are encountered. The open
fracture with calculated apertures of 1cm, 1.9cm and 3m gives the clearest response. In boreholes 12
to 15 the following features are found as a response to this open fracture:
1. The penetration rate peaks.
2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture is
encountered.
3. The large hole feeder pressure drops.
4. The rotation pressure peaks.
5. The normal hole water flow drops and gradually increases to its old level.
6. The processed penetration and rotation pressure peak.
Water pressure does not seem to be influenced by the fracture. Other discontinuities do not show an
uniform response to all boreholes. Sometimes the peaks are followed by a short small drop.
A lithological boundary at 4.8m deep in borehole 12 gives a strong response both in normal and large
hole data. Penetration rate and rotation pressure peaks. Also the Hammer pressure, feeder pressure,
water pressure and water flow of normal hole data give strong responses. Several normal hole
parameters do not return to the old level. This gives a less good response to this fracture in processed
data. On the other hand, large hole data gives a good response. In borehole 15, a calcite vein at 3.8m
gives a response in penetration rate and normalized penetration rate. Besides these two cases, there
is no clear response to a discontinuity found.
44
4.2.11 Borehole 16 to 20
The MWD data of borehole 16 is not shifted. The black area around 2.2m might be a fracture and
responses in MWD data of penetration rate, hammer pressure, feeder pressure and rotation pressure
are aligned without shifting data.
The problems with drilling large hole number 17 make it very difficult to find a proper shift. The depth
of the large hole is expected to be around 4.3m. There is however no possibility to confirm this with
MWD data, since the drilling bit has been pulled back after 5m. Two MWD logs are made and these
might overlap and that makes depth estimation of the large hole impossible. In addition, there is no
marker with response in MWD data present in the large hole data. Considering the normal hole data, it
is also difficult to come up with a good shift. Both starting depth and ending depth of drilling are
unknown. Problems with finding grip on the tunnel wall and premature depth registration make the
starting point unknown and the not finished large hole make the end depth unknown. It is chosen to
not shift data from borehole 17.
A fracture present in borehole 18 and 19 is giving responses in raw MWD parameters. With a shift of
0.2m, the rotation and feeder pressure of the normal hole data of borehole 18 are aligned with this
fracture. A 0.17m shift of large hole MWD data aligns a peak in penetration rate which is most likely
caused by this fracture. The normal hole size MWD data of borehole 19 is shifted with 0.24m a peak in
rotation pressure is aligned with this fracture. The responses to this fracture in large hole data are less
clear, but when shifting 0.26m, small changes in penetration rate and rotation pressure are aligned with
this fracture. These last shift is considered to be of fair reliability. The other shifts are of good quality.
Even though the same fracture is present in borehole 20, it is chosen to use the larger fracture at 4.5m
with an infill as a marker. There are clear responses in penetration rate, feeder pressure and rotation
pressure. With a shift in MWD data for normal hole size data of 0.22m and for large hole sized data of
0.16m, this shift is of good quality.
In boreholes 16, 18, 19 and 20 a possible fracture is at 2.2m, 4.2m, 2.1m and 1.5m is present.
Considering the orientation of the fracture and the positioning of the boreholes, this is most likely on
fracture. After some calculations, this fracture seems to have an aperture of 1.4cm, 2.3cm and 2.7cm
in boreholes 18, 19 and 20. MWD data of borehole 16 and 18, and normal hole sized MWD data of
borehole 19 show the following responses to this fracture:
1. The penetration rate peaks.
2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture is
encountered, except in borehole 19 and 20.
3. The feeder pressure drops, except in borehole 20.
4. The rotation pressure peaks.
5. The processed penetration and rotation pressure peak.
Apart from a small decrease in water flow at the fracture in borehole 16, water parameters do not show
a response on this fracture. Sometimes the peaks are followed by a short small drop. Other
discontinuities do not show an uniform response of all boreholes.
In borehole 18 at 2m, a planar discontinuity gives a response in normal size hole MWD data. It is not
clear what kind of discontinuity this is. The penetration rate decreases, rotation pressure increases and
processed penetration and rotation pressure. In borehole 19, lithological discontinuities around 1.5m
give a strong response in normal hole size MWD data. Penetration rate peaks and rotation pressure
45
drops. The fracture at a depth of 1.5m gives a response both in normal and large hole size data.
Penetration rate peaks, rotation pressure peaks and processed data gives responses.
4.2.12 Borehole 21 to 25
In all boreholes 21 to 25 there are fractures present, which are good markers. In borehole 21, the
fracture at a depth of 2.85m is used as a marker. For boreholes 22 to 24, the fractures used as a marker
are at a respective depth of 4.7m, 2.4m and 3.6m. For hole 25, two markers are needed since there are
two log files created while drilling for the normal hole size. The markers used are at 1.3m and 3.3m.
Close to 15 fractures with a greenish infill, several responses in MWD data can be observed in normal
hole size MWD data of holes 21 to 25, namely:
1. Short small peak in penetration rate, for all 15 fractures.
2. At 4 fractures the feeder pressure drops slightly.
3. A peak in rotation pressure, for 13 out of 15 fractures.
4. A drop in water flow which gradually increases to the old level, for 8 fractures.
The processed data gives shows the same, but larger, responses at the same fractures. Sometimes the
peaks are followed by a short small drop. The large hole data
5. Short small peak in penetration rate, for all 13 fractures.
6. A drop in hammer pressure for a period after encountering a fracture, for 7 of the fractures.
7. A peak in rotation pressure, for 11 out of 15 fractures.
The fractures have an aperture between 1cm and 7cm. As mentioned in section 3.4.2, the depth
registration of the televiewer was influenced by movement of the cable between the reel and tunnel
wall. The results of this are visualised in Figure 26. In borehole 21, the fracture indicated with the red
arrows has been used to shift the MWD data. This fracture is aligned with its peak in penetration rate.
The other two fractures with soft infill are not aligned with their peaks. This mismatch between the
depth of a fracture and its MWD response has been corrected by deleting a few cm of MWD data in
between these fractures. The data points which are deleted are taken spread out over this distance
between fractures, so data is scaled as evenly as possible. The data points deleted are not close to other
discontinuities. In total 11 data points are deleted in holes 21 to 23 and 25. The MWD data and
televiewer data is given in appendix 3.
In borehole 21, an approximately 4cm wide fracture zone, gives clear responses in normal hole size
MWD data. Around 0.8m, the penetration rate peaks and drops immediately after encountering intact
rock again. In borehole 22 and 24, this fracture zone around 1m and 1.3m deep, strongly influence the
large hole size MWD data. Feeder pressure and rotation pressure fluctuate and this causes responses
in processed data. In borehole 24 the penetration rate peaks. The feeder pressure and rotation pressure
peak strongly when encountering normal rock mass after the fracture zone. In borehole 23, both
normal and large hole size MWD data show responses in penetration rate, feeder pressure and rotation
pressure.
The lithological discontinuities in boreholes 21 and 24 seem to correlate with peaks in normal hole size
penetration rate and processed penetration rate. The lithological discontinuities in boreholes 22, 23
and 25 do not correlate with penetration rate as with boreholes 21 and 24.
46
In borehole 24 a new fracture zone is encountered at 0.7m. The normal hole sized penetration rate and
rotation pressure peak. The fracture zone intersects with a fracture with soft infill. This seems to affect
the rotation pressure, but there is a clear difference in penetration rate for the filled fracture and the
fracture zone.
4.2.13 Overview of observations in MWD data and mapped geology with televiewer
Overall, it can be stated that 9 regular fractures and 15 fractures with soft infill give some or all of the
listed characteristic responses below. Processed MWD data with a moving average of 4 values gives the
most accurate representation of the aperture when comparing it to mapped geology. The data
collected with the televiewer shows discontinuities with great detail. The characteristic response of
MWD data to fractures was as follows:
1. The penetration rate peaks.
2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture
is encountered.
3. The feeder pressure drops.
4. The rotation pressure peaks.
5. The normal hole water flow drops and increases gradually.
6. The processed penetration and rotation pressure peak.
The magnitude of the responses are found to be different for each fracture and no pattern in magnitude
between open and filled fractures is found. No characteristic response is found for lithological
boundaries and no consistent response is found for calcite veins. A difference in response in MWD data
for zones with low and high discontinuity spacing is not found.
A summary of the observations is given in Table 6.
4.3 Borehole camera data
As mentioned before it is difficult to distinguish discontinuities in the video footage. It is therefore
chosen to look for fluctuations in MWD data that could indicated one or multiple discontinuities and
then try to find an explanation in video footage. The difference in drilling and recorded depth is
corrected by rescaling the recorded depth over the drilling depth. The two points where the drilling and
recorded depth can be related to each other are the bottom and start of the holes. This still gives an
incorrect representation of the depth, but it is the best way to even out the difference. The MWD data
is presented in appendix 4. The depth of the large and normal hole might be shifted a little. For example
in borehole 1, the penetration rate of the normal hole shows a peak at 4.82m while the large hole
shows a peak at 4.74m. These two peaks might be a response due a geological change at one depth.
This difference can be explained by the small uncertainty of starting position of the drill bit.
Figure 26. Illustration of mismatch between televiewer depth and MWD data.
47
Apart from a few doubtful correlations between video and MWD data, no peaks or drops in parameters
are explained by discontinuities. Twice, a zone of white minerals is located at a depth where MWD data
shows peaks and drops, i.e. borehole 1 at 4.8m and borehole 4 at 3.5m (see Figure 27). Comparing
zones with fracturing and zones without fracturing is not possible since less fracture phyllite is not
encountered.
Table 6. Overview of observation in MWD data and geological data from the televiewer inspections.
Location Geological data Discontinuities observed in MWD data.
Tunnel tube A Close to profile no. 700 Borehole 1 and 2
2 borehole images with calcite veins and lithological discontinuities
- MWD parameters increase after encountering the first discontinuity - A calcite vein gives a clear response in processed data, feeder, hammer and rotation pressure
Tunnel tube A Close to profile no. 600 Borehole 3 to 10
8 borehole images with calcite veins, lithological discontinuities and fractures without infill
- MWD parameters show a clear response to 3 open fractures found in borehole 5 and 6
Tunnel tube A Close to profile no. 930 Borehole 21 to 25
5 borehole images with calcite veins, lithological discontinuities and fractures without infill
- The presence of 15 fractures with soft infill gives clear and characteristic responses in MWD data as described above - Lithological discontinuities in borehole 21 and 24 seem to correlate with peaks in penetration rate
Tunnel tube A Close to profile no. 975 and 960 Borehole 11 to 20
10 borehole images with calcite veins, lithological discontinuities, fractures with softer clayey infill and fractures without infill
- Fractures present in borehole 12, 15, 16 and 18 to 20 give a clear and characteristic response as described above
Figure 27. Zones with light coloured minerals which might give a response in MWD data. Left in borehole 1 at a depth of 4.8m and right in borehole 4 at a depth of 3.5m.
48
5. Statistical validity assessment of MWD data
As mentioned in chapter 2.7, three multivariate statistical methods believed to be useful for analysing
MWD data. As discussed in chapter 4, the datasets of MWD data and geological information obtained
by the televiewer are most suited for statistical analyses. In this chapter, the use and results of principle
component analyses, cluster analyses and multiple logistic regressions will be discussed. A visual
comparison of MWD data and discontinuities such as the one performed in chapter 4, does reveal
valuable information. Nonetheless, it is very difficult to take into account the exact responses of all 30
MWD parameters that are extracted for this thesis. Statistical analyses can help to analyse multiple
MWD parameters for multiple discontinuities at the same time. A key point is that the logistic regression
is performed several times including all or only a selection of the discontinuities, based on chapter 4.
First the different datasets will be defined.
The purpose of the statistical analyses is to identify single deterministic discontinuities. The detection
of discontinuities helps to characterise rock masses before excavation, and allows. Predicting these rock
mass conditions can improve the drill and blast process, as discussed in section 1.
5.1 Data sets
In this chapter different data sets will be used which are divided based on hole size and drilling jumbo.
Datasets 1 to 4 will be used for PCA, cluster analyses and data training with the end result of different
regression equations.
1. Dataset 1 contains the MWD data of the normal boreholes 1 and 3 till 8. Borehole 9 and 10 are
not included for the reason that a valid shift could not be found in the previous chapter.
Borehole 2 is not included. It is found that the MWD data of the normal hole of borehole 2
incorrectly represents a normal drilling process. The saw-toothed shaped raw data from 2.6m
to 0m is due to the readjustments the operator needed to make to keep the drilling arm in line
with the drilling direction. Boreholes 1 and 3 till 8 are subjected to a visual inspection where
slow starts and parts where only MWD or televiewer data is available are deleted.
2. Dataset 2 contains MWD data from the large boreholes 1, 2, 5 and 6. The other holes are not
considered since the shifts performed in the previous chapter are not reliable. Again, the slow
starts and parts with only MWD or televiewer data is deleted.
3. Dataset 3. In addition to slow starts, the parts drilled in the sandstone are deleted since average
penetration rate differs from limestone. The MWD data consists of normal hole size data from
holes 11 to 15, 18 to 22 and 24.
4. Dataset 4 consists of MWD of the large holes 11 to 15, 18 to 22 and 24. Slow starts and data of
the sandstone has been deleted.
Datasets 5 to 7 will be used to test the resulting regression equations from dataset 1 to 4.
5. Dataset 5 consists of borehole 9 and 10 and will be used to test regression equations resulting
from regression analyses of dataset 1.
6. Dataset 6 consists of borehole 3, 4 and 7 to 10 and will be used to test regression equations
resulting from regression analyses of dataset 2.
7. Dataset 7 exists of normal hole sized data from boreholes 16, 17, 23 and 25. This dataset will
be used to test the regression equations, which are made based on datasets 3.
8. Dataset 8 exists of large hole sized data from boreholes 16, 17, 23 and 25. This dataset will be
used to test the regression equations, which are made based on datasets 4.
49
These datasets will be analysed and subjected to a logistic regression analysis. Unlike the principle
component analysis and cluster analysis, an input of geological information is needed. Geological
information that is to be predicted by the multivariate logistic regression needs to be vectorised with
information on the same depths as the MWD sampling depths. The issue with the televiewer data is
that there is a difference in sampling depth accuracy and sampling depth. The televiewer data does not
fall together with a MWD sampling interval. One way of solving this is to assign a 1 in case a MWD
sampling interval is between the upper and lower depth of the discontinuity. See Table 7. If the upper
depth of a discontinuity is 2.891m and the lower depth 2.775m, a 1 is assigned to the discontinuity
location vector at the MWD sampling depths of 2.78 to 2.89. To the MWD sampling depths of 2.77 or
smaller and 2.90 and larger a 0 is assigned, until another discontinuity upper or lower boundary is
encountered. If the lower depth of a discontinuity is larger than the upper depth of a neighbouring
discontinuity, the number 1 should still be assigned.
Table 7. Table showing how depth sampling of discontinuity location vector is done.
Televiewer images. Upper depth at 2.891m and lower depth at 2.775m
Depth (m)
Discontinuity location vector
2.75 0
2.76 0
2.77 0
2.78 1
2.79 1
2.8 1
2.81 1
2.82 1
2.83 1
2.84 1
2.85 1
2.86 1
2.87 1
2.88 1
2.89 1
2.9 0
2.91 0
The problem arises when discontinuities have a large dip relative to the drilling direction. In this case
the difference between the upper and lower depth of a discontinuity is large and the numbers 1
dominate the discontinuity location vector, while MWD data might not reflect the presence as clear as
with a discontinuity with a steep discontinuity plane. In the data set this happens incidentally. A second
option for creating the discontinuity location vector is to only assign a 1 to the MWD sampling depth
closest to the single depth value of a discontinuity, or average of the lower and upper depth, from the
televiewer results. The downside with this approach is that if discontinuities influence MWD data on a
larger interval due to aperture or orientation, it could cause large mismatches between discontinuity
and MWD data. In the analysis both discontinuity location vectors will be tested and evaluated.
It is unlikely that MWD data can predict the orientation of a discontinuity based on a single hole. The
sensors do not register on which side of the borehole the drilling parameters are influenced first when
encountering a fracture. Besides this information, the televiewer data leaves 3 predictable options:
50
1. The location of a discontinuity.
2. The aperture of a discontinuity.
3. The nature of infill: softer or harder than the rock itself or absence of infill.
Creating discontinuity location vectors should lead to the possibility of testing these three options. The
comparison in chapter 4 showed an order in predictability or intensity of responses for closed, infilled
or open discontinuities. Closed discontinuities or infill fractures give a less distinctive response to
fractures without infill. This is confirmed by the literature study where Schunnesson described the
damped response of infilled fractures. This order in predictability will also be tested with the cross
tabulation of the cluster analysis. Based on this order of predictability, 4 discontinuity location vectors
are defined, where the first one contains all discontinuities and the last vector contains the open
fractures, which gives the best response. The 4 vectors included:
1. all discontinuities
2. discontinuities with an aperture>0
3. discontinuities with an aperture>0 and soft infill or no infill
4. discontinuities with an aperture>0 and no infill.
Location, aperture and nature of infill can be tested simultaneously with these four discontinuities
vectors.
5.2 Principle component analysis
PCA is used to reduce the number of input variables in the regression analysis and at the same time
retain a significant and satisfying part of the variation from the original MWD parameters. First parallel
analyses are used to determine an appropriate number of principle components. Then the type of
rotations used in the principle component are discussed. Finally the results of the principle component
analyses for each dataset are given.
5.2.1 Determining the number of principle components
Since the data is mostly non-Gaussian, it is chosen to perform parallel analyses based on both normally
distributed random data and permutations of the input data. For these analyses the SPSS syntax written
by B.P. O’Connor is used (O'Connor, 2000). It is chosen to create 1000 datasets and the statistically
significance level is set to 95%.
The scree plots of the two parallel analysis and the scree plot of the dimension reduction by SPSS are
displayed in Figure 28. All methods indicate that there are 7 statistically significant components. The
parallel generated eigenvalues of the 8th component are larger than eigenvalue of the 8th component
of the original dimension reduction analysis, which is described as the benchmark for the decision of
the number of components (section 2.7.1). Dimension reduction of dataset 1 leads to 7 statistically
significant principle components.
Dataset 2 is smaller and therefore has a shorter computation times. In appendix 8 the outcome of both
parallel analyses is given. Unlike for data set 1, the SPSS evaluation and parallel analyses are not
coherent. SPSS indicates to use the 6 components with eigenvalues larger than 1, while both parallel
analyses indicate only 5 statistically significant components. Dimension reduction of dataset 2 leads to
5 components.
For both dataset 3 and 4, SPSS indicates that 7 principle components are appropriate. Both parallel
analyses indicate that 7th principle components is not statistically significant. The 7th eigenvalues of the
51
parallel analyses with the 95th percentile exceeds the eigenvalue of the SPSS raw data. The scree plots
can be found in appendix 8. Dimension reduction leads to 6 principle components.
5.2.2 Orthogonal and oblique rotation
In Table 8 the correlations between principle components with an oblique rotation are displayed for
the datasets. The correlation matrix of the principle components of the orthogonal rotation is an
identity matrix. The highest correlations for dataset 1 are 0.22, 0.27, -0.28 and 0.29. For dataset 2 the
highest correlations are -0.24 0.23 and -0.21 and the remaining correlations are below 0.2. As described
in the section 2.7.1 a correlation of 0.32 means there is an overlap of 10% in variance among principle
components. Principle components 1 and 5 in dataset 3 have an overlap of more than 10% in variance,
as the Pearson correlation is -0.37. The same counts for components 5 and 6 with a correlation of 0.42.
These do not exceed 0.8 which would likely indicate collinearity, as mentioned in section 2.7.4. It is
chosen to continue with the outcomes of the PCA with the oblique rotation direct oblimin, since this
might represents the inherent correlations between the principle components better.
Table 8. Component correlation matrices with an oblique rotation for all datasets. Highlighted in green are the highest Pearson correlations.
Component Correlation Matrix Dataset 1
Component 1 2 3 4 5 6 7
1 1.00 .03 .27 .15 -.17 -.28 .29
2 .03 1.00 -.02 .12 -.19 .08 -.02
3 .27 -.02 1.00 .22 -.16 -.07 .1
4 .15 .12 .22 1.00 -.13 .12 .12
5 -.17 -.19 -.16 -.13 1.00 .05 -.05
6 -.28 .08 -.07 .12 .05 1.00 -.16
7 .29 -.02 .10 .12 -.05 -.16 1.00
0
1
2
3
4
5
6
7
8
9
10
11
12
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Eige
nva
lue
Component number
Scree plot PCA and parallel analysis dataset 1
Eigenvalues calculatedby SPSS dimensionreduction
Eigenvalues parallelanalysis raw datapermutation
Eigenvalues parallelanalysis randomnormal datageneration
Figure 28. Scree plot for dataset 1 to determine the number of statistically significant components.
52
Component Correlation Matrix Dataset 2
Component 1 2 3 4 5
1 1.00 .07 .23 -.02 -.08
2 .07 1.00 .06 -.24 -.22
3 .23 .06 1.00 .03 -.09
4 -.01 -.24 .03 1.00 .11
5 -.08 -.22 -.09 .11 1.00
Component Correlation Matrix Dataset 3
Component 1 2 3 4 5 6
1 1.00 -.01 -.00 -.29 -.37 .15
2 -.01 1.00 .04 .22 -.15 -.22
3 -.00 .04 1.00 -.22 -.14 .01
4 -.29 .22 -.22 1.00 .08 -.30
5 -.37 -.15 -.14 .08 1.00 .06
6 .15 -.22 .01 -.30 .06 1.00
Component Correlation Matrix Dataset 4
Component 1 2 3 4 5 6
1 1.00 -.25 -.18 -.17 .07 .22
2 -.25 1.00 -.05 .18 .06 -.03
3 -.18 -.05 1.00 -.04 -.27 -.07
4 -.17 .18 -.04 1.00 .04 .04
5 .07 .06 -.27 .04 1.00 .42
6 .22 -.03 -.07 .04 .42 1.00
5.2.3 PCA outcome dataset 1
The Kaiser-Meyer-Oklin measure of sampling adequacy equals 0.801. All communalities are larger than
0.78 except for RMS normalized water with a moving average of 8 values and water pressure, where
0.508 and 0.153 percent of their variances is being accounted for by the PCA (appendix 8). Both the
KMO test and the communalities indicate that the use of a PCA is justified.
In Table 9 it can be seen how much of the total variance of the variables is explained by which
component. Automatically SPSS first computes the component which contains most of the variance,
36.082%. The total variance explained by all 7 principle components is 86.8%. The 7th component
explains only 4.2% of the variance.
53
Table 9. Total variance explained by principle components.
Component Extraction Sums of Squared Loadings
Component Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
Dataset 1 Dataset 3
1 10.8 36.1 36.1 1 8.0 25.7 25.7
2 4.4 14.8 50.9 2 5.9 18.9 44.5
3 3.5 11.6 62.5 3 4.6 14.9 59.4
4 2.4 7.9 70.4 4 2.7 8.7 68.2
5 1.9 6.5 76.8 5 2.0 6.4 74.6
6 1.7 5.7 82.6 6 1.5 4.7 79.2
7 1.3 4.2 86.8 Dataset 4
Dataset 2 1 7.9 25.5 25.5
1 11.6 38.7 38.7 2 5.6 18.2 43.7
2 6.2 20.7 59.4 3 4.0 12.8 56.5
3 3.8 12.6 72.0 4 2.9 9.4 66.0
4 2.6 8.7 80.8 5 2.3 7.5 73.4
5 1.5 5.0 85.8 6 1.3 4.2 77.7
In Table 10 the pattern matrix of the PCA is given. This matrix contains the correlations between
components and explanatory variables. It should be noted that ideally a variable should be correlated
with only one component. Both for an oblique rotation, as show in the table, and an orthogonal rotation
this is not the case. However, most variables seem to be correlated highly with one component. These
variables are marked green. There are 6 variables that show a similar correlation with 2 or 3
components, these are marked in yellow. As can be seen Table 10, water pressure has no good
correlation with any of the components as well as RMS normalized water MA 8.
Which variable is represented by which component is a delicate interpretation. A few careful
statements can be made about each component.
- Component 1. Three out of four normalized rotation pressures are positively correlated with
component 1. The fourth normalized rotation pressure is not correlated significantly with
component 1. In addition, processed water pressure and flow seems to be good and medium
correlated with component 1.
- Component 2. Two out of four normalized water variables have a medium correlation with
component 2. Four of the raw drilling parameters are strongly correlated with component 2.
- Component 3. This component seems to represent the normalized penetration and the
penetration rate. One of the normalized penetrations is also correlated with component 1.
- Component 4. Four out of three RMS normalized penetrations are strongly correlated with this
component.
- Component 5. This component is correlated with two RMS normalized rotation pressures, one
normalized rotation pressure and the raw rotation pressure.
- Component 6. This component is correlated with the remaining RMS normalized rotation
pressure and the two remaining RMS normalized water variables, although one is shared with
2 other components.
54
- Component 7. The remaining RMS normalized penetration and rotation pressure are correlated
with this component.
Table 10. Pattern matrix giving the correlation between parameters and principle components for dataset 1.
In appendix 8 the component score coefficient matrix is given. These weights can be used to calculate
the component scores. The weights should be multiplied with the standardized variables. The total sum
of these multiplications lead to the scores.
Component
1 2 3 4 5 6 7
Normalized rotation pressure MA 2 normal hole (%) .935 -.102 .135 .151 .080 -.031 -.188
Normalized rotation pressure MA 6 normal hole (%) .817 -.061 .102 .134 .023 -.084 .223
Normalized rotation pressure MA 8 normal hole (%) .809 -.067 .098 .147 .067 -.088 .234
RMS normalized water MA 6 normal hole (%) -.796 .044 -.083 -.008 .164 .142 -.145
RMS normalized water MA 2 normal hole (%) -.787 .004 -.118 -.087 .126 .122 -.201
Normalized water MA 2 normal hole (%) -.777 .168 -.094 -.086 .139 .128 -.190
Normalized water MA 4 normal hole (%) .662 .510 .126 -.034 -.034 .586 .031
Normalized water MA 8 normal hole (%) .651 .487 .147 .076 -.037 -.010 .225
Normalized water MA 6 normal hole (%) .088 .881 .121 .008 .126 .042 .074
Water flow normal hole (l/min) -.024 .809 .187 -.033 -.186 -.143 .013
Feeder pressure normal hole (bar) .207 -.757 .299 -.043 .202 -.058 .053
Hammer pressure normal hole (bar) .271 -.688 .404 -.071 -.035 -.152 -.022
Water pressure normal hole (bar) -.039 .265 -.064 .240 -.009 -.039 -.132
Normalized penetration MA 2 normal hole (%) .130 .009 .895 .076 -.052 -.004 -.180
Penetration rate normal hole (m/min) .172 -.167 .848 .030 -.075 -.068 -.163
Normalized penetration MA 8 normal hole (%) .045 .085 .835 .150 -.081 -.020 .199
Normalized penetration MA 4 normal hole (%) -.767 .056 .819 .013 .046 .100 -.012
Normalized penetration MA 6 normal hole (%) .200 .054 .777 .131 -.098 -.037 .210
RMS normalized penetration MA 6 normal hole (%) .109 -.005 .069 .913 -.069 -.101 .009
RMS normalized penetration MA 8 normal hole (%) .132 -.015 .011 .885 -.102 -.107 .037
RMS normalized penetration MA 4 normal hole (%) .107 .030 .145 .826 -.021 .081 -.100
RMS normalized rotation pressure MA 8 normal hole (%) -.123 -.123 -.041 .154 -.911 .186 .106
RMS normalized rotation pressure MA 6 normal hole (%) -.039 -.134 -.010 .178 -.898 .150 .019
Normalized rotation pressure MA 4 normal hole (%) -.139 -.284 -.138 .171 .755 .198 .034
Rotation pressure normal hole (bar) -.120 -.282 -.148 .157 .742 .195 .048
RMS normalized rotation pressure MA 4 normal hole (%) .003 -.067 -.055 -.081 -.218 .844 -.286
RMS normalized water MA 4 normal hole (%) -.139 -.015 -.053 -.105 .042 .841 -.153
RMS normalized water MA 8 normal hole (%) -.193 -.005 .066 .360 .082 .452 .341
RMS normalized penetration MA 2 normal hole (%) -.107 -.012 .107 .142 .090 .152 -.897
RMS normalized rotation pressure MA 2 normal hole (%) -.155 -.027 .020 -.032 .041 .178 -.855
55
5.2.4 PCA outcome dataset 2
The Kaiser-Meyer-Oklin measure of sampling adequacy is 0.827. This indicates that a PCA is an
appropriate data reduction method. The communalities are given in appendix 8, the lowest
communality is 0.267 for the RMS normalized water variables with a moving averages of 8 values. The
other variables have communalities of 0.606 and higher. This confirms the justification for the use of
PCA.
The total variance explained by the 5 principle components is 85.8%, where the 1st component explains
38.7% and the 5th component explains 5.0% of the total variance (Table 9).
- Component 3 represents normalized water and water flow.
- Component 4 represents RMS normalized penetration.
- Component 5 represents RMS normalized water.
- Component 6 represents RMS normalized rotation pressure.
In appendix 8 the component score matrix is given.
Table 11 is the pattern matrix of the PCA of dataset 2. Unlike for dataset 1 there are several variables,
which are correlated well with one component and correlated medium with another component. The
following statements can be made about which variables are represented by which components.
- Component 1. This components contains 10 variables. Three out of the four normalized
rotation pressures and normalized water variables are correlated with this components. In
addition there are 4 individual variables correlated to this component.
- Component 2. Three out of four RMS normalized penetrations and RMS normalized rotation
pressures are correlated by this component.
- Component 3. This component is correlated with water pressure and normalized water.
- Component 4. Three of the raw drilling parameters are correlated with this component.
- Component 5. Penetration rate and three out of four normalized penetrations are correlated
are partly correlated with this component.
In appendix 8 the component score matrix is given.
5.2.5 PCA outcome dataset 3
The Kaiser-Meyer-Oklin measure of sampling adequacy equals 0.824. All communalities are larger than
0.602 except for water pressure and rotational speed, where 0.087 and 0.159 percent of their variances
is being accounted for by the PCA (appendix 8). Both the KMO test and the communalities indicate that
the use of a PCA can be justified.
In Table 9 it can be seen how much of the total variance of the variables is explained by which
component. Automatically SPSS first computes the component which contains most of the variance,
25.7%. The total variance explained by all 6 principle components is 79.2%. The 6th component explains
only 4.7% of the variance.
In Table 12 the pattern matrix of the PCA is given. While the division of parameters among components
for dataset 1 and 2 was mixed, dataset 3 groups parameters according to suite.
- Component 1 represents normalized penetration, penetration rate, hammer pressure and
feeder pressure.
- Component 2 represents normalized rotation pressure and rotation pressure.
- Component 3 represents normalized water and water flow.
56
- Component 4 represents RMS normalized penetration.
- Component 5 represents RMS normalized water.
- Component 6 represents RMS normalized rotation pressure.
In appendix 8 the component score matrix is given.
Table 11. Pattern matrix giving the correlation between parameters and principle components for dataset 2.
Component
1 2 3 4 5
Normalized rotation pressure MA 6 large hole (%) .972 .065 -.006 .096 .003
Normalized rotation pressure MA 8 large hole (%) .970 -.002 -.103 .157 .012
Normalized rotation pressure MA 2 large hole (%) .954 .112 .068 .051 -.015
Normalized water MA 2 large hole (%) -.945 -.111 .412 .102 -.015
RMS normalized water MA 6 large hole (%) -.927 -.046 -.142 .077 .074
RMS normalized water MA 2 large hole (%) -.913 -.078 -.187 .074 .036
RMS normalized penetration MA 2 large hole (%) .857 .208 .153 -.086 -.054
RMS normalized rotation pressure MA 2 large hole (%) .854 .194 .163 -.105 -.041
Normalized penetration MA 4 large hole (%) -.843 .095 -.147 .036 -.516
RMS normalized water MA 4 large hole (%) -.718 .236 -.014 -.024 .220
RMS normalized penetration MA 6 large hole (%) .192 .855 .055 .046 -.130
RMS normalized penetration MA 4 large hole (%) .092 .805 .040 -.009 -.130
RMS normalized rotation pressure MA 6 large hole (%) -.126 .792 -.071 -.280 -.017
RMS normalized rotation pressure MA 8 large hole (%) .234 .716 -.042 -.158 -.143
RMS normalized rotation pressure MA 4 large hole (%) -.397 .704 -.062 -.255 .088
RMS normalized penetration MA 8 large hole (%) .429 .703 .070 .134 -.194
Normalized water MA 6 large hole (%) -.027 -.048 .972 .032 -.068
Water pressure large hole (bar) .050 .040 -.950 -.003 .038
Normalized water MA 4 large hole (%) .367 .086 .805 -.032 -.019
Normalized water MA 8 large hole (%) .522 .011 .718 -.010 -.070
RMS normalized water MA 8 large hole (%) -.099 .212 .357 .254 .274
Feeder pressure large hole (bar) -.070 -.290 .147 .794 .011
Rotation pressure large hole (bar) .043 .033 -.470 .789 .050
Hammer pressure normal large (bar) .001 -.203 .311 .767 -.213
Normalized rotation pressure MA 4 large hole (%) .017 -.056 -.612 .618 .078
Penetration rate large hole (m/min) .210 .188 .110 .141 -.835
Normalized penetration MA 6 large hole (%) -.423 .258 -.034 -.002 -.831
Normalized penetration MA 2 large hole (%) .203 .255 .064 -.093 -.800
Normalized penetration MA 8 large hole (%) -.661 .202 -.079 .041 -.715
Water flow large hole (l/min) .269 -.161 .460 -.052 -.603
57
Table 12. Pattern matrix giving the correlation between parameters and principle components for dataset 3.
Component
1 2 3 4 5 6
Normalized penetration MA 4 normal hole (%) .974 .032 .059 .032 .025 .011
Normalized penetration MA 6 normal hole (%) .955 .018 .048 .014 -.002 .015
Normalized penetration MA 2 normal hole (%) .953 .048 .065 .055 .061 .021
Normalized penetration MA 8 normal hole (%) .927 .013 .033 -.006 -.016 .030
Penetration rate normal hole (m/min) .887 -.001 -.011 .100 -.016 -.005
Hammer pressure normal hole (bar) .771 -.076 -.213 -.047 -.209 .044
Feeder pressure normal hole (bar) .698 -.154 -.230 -.075 -.076 .037
Rotation speed normal hole (rpm) -.249 -.077 -.123 .208 .005 .026
Normalized rotation pressure MA 4 normal hole (%) -.014 .966 -.014 -.002 -.033 -.010
Normalized rotation pressure MA 2 normal hole (%) .052 .956 -.020 -.010 .016 -.014
Normalized rotation pressure MA 6 normal hole (%) -.053 .953 -.008 .008 -.059 -.003
Normalized rotation pressure MA 8 normal hole (%) -.080 .934 -.009 .023 -.076 .011
Rotation pressure normal hole (bar) .078 .865 -.044 -.020 .029 -.004
Normalized water MA 6 normal hole (%) -.031 .006 .952 .002 -.093 .006
Normalized water MA 4 normal hole (%) -.023 .001 .945 .010 -.126 -.026
Normalized water MA 8 normal hole (%) -.031 .015 .940 -.009 -.055 .023
Normalized water MA 2 normal hole (%) -.011 -.008 .880 -.006 -.142 -.084
Water flow normal hole (l/min) .057 -.102 .753 -.030 .205 .092
RMS normalized penetration MA 6 normal hole (%) -.018 -.003 -.029 .944 -.082 -.032
RMS normalized penetration MA 4 normal hole (%) .038 .027 -.024 .932 -.034 -.051
RMS normalized penetration MA 8 normal hole (%) -.069 -.022 -.034 .901 -.091 -.004
RMS normalized penetration MA 2 normal hole (%) .141 .063 .048 .763 .059 -.093
RMS normalized water MA 6 normal hole (%) .041 .041 .002 .094 -.937 .024
RMS normalized water MA 8 normal hole (%) .030 .081 -.071 .115 -.908 .046
RMS normalized water MA 4 normal hole (%) .047 .032 .106 .024 -.897 .011
RMS normalized water MA 2 normal hole (%) -.016 .005 .163 -.096 -.758 -.025
RMS normalized rotation pressure MA 4 normal hole (%) -.079 .086 .031 .177 .113 -.832
RMS normalized rotation pressure MA 6 normal hole (%) -.115 .015 -.005 .200 .102 -.830
RMS normalized rotation pressure MA 8 normal hole (%) -.161 -.039 -.005 .213 .065 -.782
RMS normalized rotation pressure MA 2 normal hole (%) -.027 .171 .011 .030 .114 -.725
Water pressure normal hole (bar) .057 -.039 -.018 -.086 -.098 -.278
5.2.6 PCA outcome dataset 4
The Kaiser-Meyer-Oklin measure of sampling adequacy is 0.806. This indicates that a PCA is an
appropriate data reduction method. The communalities are given in appendix 8, the lowest
communalities are 0.326 and 0.141 for the penetration rate and rotational speed. The other variables
have communalities of 0.569 and higher. This justifies the use of PCA.
The total variance explained by the 6 principle components is 77.7%, where the 1st component explains
25.5% and the 6th component explains 4.2% of the total variance (Table 9).
58
Table 13. Pattern matrix giving the correlation between parameters and principle components for dataset 4.
Component
1 2 3 4 5 6
Hammer pressure normal large (bar) -.881 .081 .098 .069 .027 .063
Feeder pressure large hole (bar) -.858 .096 -.164 -.044 .057 -.011
Normalized penetration MA 4 large hole (%) .780 -.032 -.207 -.023 .031 .245
Normalized penetration MA 6 large hole (%) .779 -.029 -.201 -.036 .059 .249
Normalized penetration MA 2 large hole (%) .758 -.036 -.203 -.016 -.013 .217
Normalized penetration MA 8 large hole (%) .744 .003 -.179 -.049 .115 .173
Penetration rate large hole (m/min) -.551 .040 -.066 .053 -.039 .269
Normalized water MA 2 large hole (%) .051 -.933 -.008 .040 -.008 -.001
Normalized water MA 4 large hole (%) .115 -.924 .036 .022 .002 .002
Normalized water MA 6 large hole (%) .154 -.877 .064 -.032 .011 .013
Water flow large hole (l/min) .125 -.830 .046 .023 -.069 .101
Water pressure large hole (bar) -.271 -.826 -.098 -.017 .056 -.104
Normalized water MA 8 large hole (%) .119 -.812 .078 -.068 -.018 .079
Normalized rotation pressure MA 2 large hole (%) .006 .006 -.940 .022 -.045 .016
Normalized rotation pressure MA 4 large hole (%) .084 .024 -.930 .038 -.003 -.034
Normalized rotation pressure MA 6 large hole (%) .113 .035 -.908 .044 .024 -.055
Normalized rotation pressure MA 8 large hole (%) .110 .034 -.869 .056 .060 -.048
Rotation pressure large hole (bar) -.056 -.027 -.841 -.026 -.043 .034
RMS normalized water MA 6 large hole (%) -.051 -.083 -.061 .953 .005 -.007
RMS normalized water MA 4 large hole (%) -.014 .017 -.027 .926 .012 -.016
RMS normalized water MA 8 large hole (%) -.094 -.148 -.065 .909 -.005 -.010
RMS normalized water MA 2 large hole (%) .108 .244 .072 .688 .042 .010
RMS normalized rotation pressure MA 4 large hole (%) .020 -.046 .043 .023 .905 .106
RMS normalized rotation pressure MA 6 large hole (%) .004 -.012 .040 .022 .884 .138
RMS normalized rotation pressure MA 2 large hole (%) .035 -.015 .054 .032 .834 -.051
RMS normalized rotation pressure MA 8 large hole (%) .037 .058 .038 .032 .832 .071
Rotation speed large hole (rpm) .060 -.011 .201 .054 -.282 .034
RMS normalized penetration MA 4 large hole (%) -.002 -.010 .015 -.004 .049 .927
RMS normalized penetration MA 6 large hole (%) -.003 -.006 .053 -.015 .096 .906
RMS normalized penetration MA 8 large hole (%) .022 .006 .076 -.007 .108 .836
RMS normalized penetration MA 2 large hole (%) .043 -.033 -.034 .002 .025 .749
Table 13 is the pattern matrix of the PCA of dataset 2. Unlike for dataset 1 there are several variables,
which are correlated well with one component and correlated medium with another component. The
following statements can be made about which variables are represented by which components.
- Component 1 represents normalized penetration, penetration rate, hammer pressure and
feeder pressure.
- Component 2 represents normalized water, water pressure and water flow.
- Component 3 represents normalized rotation pressure and rotation pressure.
59
- Component 4 represents RMS normalized water.
- Component 5 represents RMS normalized rotation pressure.
- Component 6 represents RMS normalized penetration.
In appendix 8 the component score matrix is given.
5.2.7 Overview of PCA analyses
Table 14 gives a short overview of the outcome of the principle component analyses.
Table 14. Overview of PCA analyses.
Dataset Extraction Principle components
1 7 components retaining 86.8% of the variability
Principle components contain a mix of parameters and often one parameter is dominant in a component
2 5 components retaining 85.8% of the variability
3 6 components retaining 79.2% of the variability
- All principle components represent a group of processed parameters and their raw parameters - Rotational speed is not well correlated with any of the principle components
4 6 components retaining 77.7% of the variability
5.3 Cluster analysis
The goal of this analysis is to identify and understand possible clusters in the data. These clusters might
represent measurements in rock without discontinuities, with bedding discontinuities, discontinuities
with an infill and an aperture, discontinuities without infill and an aperture or a combination of
discontinuities. Unlike supervised methods like regression where accuracy can be measured, there is
no objective way of to measure if the clustering introduced by k-means analysis is any good.
5.3.1 K-means cluster analysis
Like described in the section 2.7.2, a k-means cluster analysis is capable of handling large amounts of
data. It is chosen to run the cluster analysis for the groups of standardized MWD data and the principle
components for k-values from 2 to 10. The outcome of Variance Ration Criterion (VRC) is given in Table
15. The lowest 𝜔𝑘 value indicates the ‘correct’ number of clusters. In addition it is important to choose
the number of clusters for which the iterations are low.
Table 15. The outcome of Variance Ration Criterion.
Number of clusters
𝝎𝒌 for MWD clusters dataset
1
𝝎𝒌 for principle component
clusters dataset 1
𝝎𝒌 for MWD clusters dataset
2
𝝎𝒌 for principle component
clusters dataset 2
3 534831 4842 50460.46 192.80
4 370152 5571 14868.23 396.22
5 209394 25 1484.25 3646.96
6 45497 2060 3225.05 3434.84
7 28777 330 12356.72 1034.55
8 13300 2031 255.24 1130.49
9 17025 761 19555.68 1166.60
10 18807 7395 19032.77 5464.08
60
Number of clusters
𝝎𝒌 for MWD clusters dataset
3
𝝎𝒌 for principle component
clusters dataset 3
𝝎𝒌 for MWD clusters dataset
4
𝝎𝒌 for principle component
clusters dataset 4
3 3495.44 793.43 6566.26 608.32
4 7894.45 1296.42 1775.94 1428.48
5 4996.48 293.90 1761.48 1974.87
6 556.15 43.30 2069.11 1752.62
7 260.80 21.38 1720.32 711.63
8 529.42 286.72 195.13 106.52
9 653.28 164.25 837.28 218.36
10 461.92 1659.93 706.11 1326.43
For the MWD clusters of dataset 1 the best number of clusters is 8 and for principle component the
best number of clusters is 5. Both the VRC and the iterations indicate this. For dataset 2, the VRC and
iterations do not point at the same number of clusters. The lowest 𝜔𝑘 value for MWD clusters is 255.24
for 8 clusters, but there are 19 iterations needed to create the 8 clusters. For the principle component
clusters, VRC indicates 3 and 4 clusters while iterations are 23 and 20. In both these cases, it is chosen
to pick a higher number of clusters, so cross tabulation in the next part will not hide information. For
the MWD and principle component clusters of dataset 2, 8 and 7 clusters are chosen. The VRC indicates
7 clusters for both MWD and principle components clusters. 17 and 19 iterations are required to come
to a stable division of clusters in the MWD and principle component dataset. For dataset 4, 15 iterations
were needed to come to 8 clusters for both MWD and principle component dataset.
5.3.2 Cross tabulation clusters and discontinuity groups
The way the clusters are evaluated is by looking at the number of samples or percentage of samples in
a cluster and compare this to the number of samples with a specific discontinuity. A discontinuity is
either present or not, which makes a cluster analysis like k-means with hard boundaries suited for the
cross tabulation with discontinuities. In order to do this 5 discontinuity groups are defined.
1. A group with samples of no discontinuities
2. A group with samples of lithological discontinuities
3. A group with samples of discontinuities with calcite infill and an aperture>0
4. A group with samples of discontinuities with soft infill and an aperture>0
5. A group with samples of discontinuities without infill and an aperture>0
As observed in the visual assessment of the MWD data, it is difficult to differentiate samples from group
1 and 2 and easier to differentiate between samples from group 4 or 5 and other groups. It could be
possible that groups 1 and 2 cluster together and groups 3, 4 and/or 5 cluster together. In order to be
able to observe these characteristics better, the clusters are cross tabulated with the 5 different
discontinuity groups.
5.3.2.1 Dataset 1
The MWD cross tabulation is given in Table 16. It can be seen that cluster 7 separates 38.3% of the
discontinuities with calcite infill together with 95 samples from other groups. Clusters 2, 4, 6 and 8
capture 79.6% of the samples with no discontinuity and 93.7% of samples with lithological
discontinuities. These two groups cannot be separated by the cluster analysis. Nine samples or 47.4%
of discontinuities without infill and an aperture are separated after 6 iterations in clusters 4 and 6.
Clusters 4 and 6 contain 25 other samples.
61
In Table 17 the cross tabulation of the principle component clusters is given. Similar observations as
with MWD clusters can be made. Clusters 2, 4 and 5 capture 86.9% of the samples with no discontinuity
and 95.2% of samples with lithological discontinuities. These clusters contain only a few samples of
other groups. Cluster 2 contains nearly only samples with no discontinuity. 39.8% of samples with
discontinuities with calcite infill are captured together with 109 other samples in cluster 3. 47.4% or 9
samples of discontinuities without infill and an aperture are separated by cluster 1 together with 20
samples belong to other groups.
Overall it seems that there is a correlation between clusters and discontinuity groups, even though
there are no clusters only containing samples from a single discontinuity group. The challenge with
dataset 1 is the difference in sample size between discontinuity groups.
Table 16. Cross tabulation of MWD clusters and discontinuity groups for dataset 1.
Table 17. Cross tabulation of principle component clusters and discontinuity groups for dataset 1.
Cluster 1 2 3 4 5 Total
Iterations 6 5 8 8 6 -
Number of cases 29 73 187 1099 203 1591
Percentage 1.8% 4.6% 11.8% 69.1% 12.8% 100.0%
Discontinuity 4 groups (no, bedding, infill & aperture>0 and no infill & aperture>0) * Cluster of 5-means analysis principle components
No disc. 14 72 78 423 114 701
2.0% 10.3% 11.1% 60.3% 16.3% 100.0%
Lithological disc. 6 0 26 569 72 673
.9% 0.0% 3.9% 84.5% 10.7% 100.0%
Disc with calcite infill and an aperture
0 1 78 102 15 196
0.0% .5% 39.8% 52.0% 7.7% 100.0%
Disc. Without infill and with an aperture
9 0 5 3 2 19
47.4% 0.0% 26.3% 15.8% 10.5% 100.0%
Cluster 1 2 3 4 5 6 7 8 Total
Iterations 3 4 6 14 6 14 12 2 -
Number of cases 7 106 27 538 92 582 166 73 1591
Percentage 0.4% 6.7% 1.7% 33.8% 5.8% 36.6% 10.4% 4.6% 100.0%
Discontinuity 4 groups (no, bedding, infill & aperture>0 and no infill & aperture>0) * Cluster of 8-means analysis MWD data
No disc. 0 51 19 191 58 244 66 72 701
0.0% 7.3% 2.7% 27.2% 8.3% 34.8% 9.4% 10.3% 100.0%
Lithological disc. 3 55 3 291 17 285 19 0 673
.4% 8.2% .4% 43.2% 2.5% 42.3% 2.8% 0.0% 100.0%
Disc with calcite infill and an aperture
0 0 0 54 15 50 76 1 196
0.0% 0.0% 0.0% 27.6% 7.7% 25.5% 38.8% .5% 100.0%
Disc. Without infill and with an aperture
4 0 5 0 2 3 5 0 19
21.1% 0.0% 26.3% 0.0% 10.5% 15.8% 26.3% 0.0% 100.0%
62
5.3.2.2 Dataset 2
The MWD cross tabulation is given in Table 18. 50% of the samples without infill and with an aperture
are separated after 4 iterations in clusters 2 and 5. There are only 4 samples of the group with no
discontinuities in these two clusters. 96.5% of the samples with lithological discontinuities are clustered
together with 52.2% of the samples with no discontinuities. 11.6% of the discontinuities with calcite
infill are separated after 1 iteration in cluster 6. As in dataset 1, it looks as if half the discontinuities
without infill and an aperture can be separated and that the groups ‘no discontinuities’ and lithological
discontinuities cluster together.
In Table 19 the cross tabulation of the principle component clusters is given. After 3 iterations, 40% of
the samples with discontinuities without infill and an aperture are separated together with only one
other sample. 10.7% of the samples with calcite infilled discontinuities are separated in cluster 1. It
takes up to 10 iterations to create the clusters 2, 3, 6 and 7, which represent a mix of samples with no
discontinuities and lithological discontinuities. The cross tabulation of dataset 2 is similar to the cross
tabulation of dataset 1. A fair part of open fractures can be separated and lithological discontinuities
and intact rock cluster together.
Table 18. Cross tabulation of MWD clusters and discontinuity groups for dataset 2.
Cluster 1 2 3 4 5 6 7 8 Total
Iterations 3 4 3 18 2 1 11 18 -
Number of cases 192 9 6 311 5 14 13 205 755
Percentage 25.4% 1.2% .8% 41.2% .7% 1.9% 1.7% 27.2% 100.0%
Discontinuity 4 groups (no, bedding, infill & aperture>0 and no infill & aperture>0) * Cluster of 8-means analysis MWD data
No disc. 129 4 2 213 0 0 7 117 472
27.3% .8% .4% 45.1% 0.0% 0.0% 1.5% 24.8% 100.0%
Lithological disc. 53 0 3 2 0 0 0 84 142
37.3% 0.0% 2.1% 1.4% 0.0% 0.0% 0.0% 59.2% 100.0%
Disc with calcite infill and an aperture
5 0 0 96 0 14 6 0 121
4.1% 0.0% 0.0% 79.3% 0.0% 11.6% 5.0% 0.0% 100.0%
Disc. Without infill and with an aperture
5 5 1 0 5 0 0 4 20
25.0% 25.0% 5.0% 0.0% 25.0% 0.0% 0.0% 20.0% 100.0%
5.3.2.2 Dataset 3 and 4
The cross tabulation of dataset 3 and 4 is in contrast with the cross tabulation of dataset 1 and 2.
Samples are spread out over all clusters. There seems to be no cluster that represents a fair amount of
a discontinuity group including a small number of samples from other groups. Apart from a few
exceptions, iterations are high. This indicates that the k-means cluster analyses had to shift the centre
points of the clusters often until a stable division of clusters was reached. A possible reason could be
that responses to fractures are not significant enough to be separated by k-means cluster. The cross
tabulation of dataset 3 and 4 can be found in appendix 9.
63
Table 19. Cross tabulation of PC clusters and discontinuity groups for dataset 2.
Cluster 1 2 3 4 5 6 7 Total
Iterations 3 18 15 16 3 19 19 -
Number of cases 14 165 196 18 9 207 146 755
Percentage 1.9% 21.9% 26.0% 2.4% 1.2% 27.4% 19.3% 100.0%
Discontinuity 4 groups (no, bedding, infill & aperture>0 and no infill & aperture>0) * Cluster of 7-means analysis MWD data
No disc. 0 93 131 9 0 119 120 472
0.0% 19.7% 27.8% 1.9% 0.0% 25.2% 25.4% 100.0%
Lithological disc. 0 2 55 1 0 84 0 142
0.0% 1.4% 38.7% .7% 0.0% 59.2% 0.0% 100.0%
Disc with calcite infill and an aperture
13 70 5 6 1 0 26 121
10.7% 57.9% 4.1% 5.0% .8% 0.0% 21.5% 100.0%
Disc. Without infill and with an aperture
1 0 5 2 8 4 0 20
5.0% 0.0% 25.0% 10.0% 40.0% 20.0% 0.0% 100.0%
5.3.3 Overview of cluster analysis outcome
Table 20 gives a short overview of the cluster analysis outcomes.
Table 20. Overview if cluster analysis outcome.
Dataset Cluster content
1 - Lithological discontinuities cluster together - A small number of data points with discontinuities with calcite infill cluster together - A substantial number of data points with open fractures cluster together
2
3 and 4 - Samples are spread over all clusters
5.4 Logistic regression
Here the results of the logistic regressions are discussed. For each dataset, separate regressions are
performed for the 4 discontinuity location vectors discussed in section 5.1. The cluster analyses of
dataset 1 and 2 confirmed the order of predictability on which the discontinuity location vectors are
based By studying the Pearson’s correlations between the two types of discontinuity vectors, containing
only the average position or upper and lower boundary, it seemed that the vectors containing only the
average are correlated much less than the vectors containing upper and lower boundaries. Therefor
the discontinuity vector containing the average location is disregarded.
5.4.1 Training dataset 1
In appendix 10 the Pearson’s correlations between the principle components and the discontinuity
location vectors is given. These correlations are not high, even though SPSS indicates a number as
significant. The relevant SPSS results are presented in appendix 11.
5.4.1.1 All discontinuities
This vector contains 888 samples with a discontinuity present and 701 with no discontinuity present.
This gives a predictive capacity 55.9% when considering no explanatory variables.
64
For all the 5 generated possibilities by SPSS that meet the condition of the Wald statistics, the Omnibus
Test of Model Coefficients shows that the generated possibilities are an improvement from the model
without explanatory variable. However the Hosmer and Lemeshow’s test indicates that for none of the
generated models the data fits with the logistic regression models. The p-values are 0. The fact that the
sample number is high and the ratio ones and zeros in the discontinuity location vector is good for the
Hosmer and Lemeshow’s test shows that the data including all discontinuities undoubtedly does not fit
the logistic regression model.
5.4.1.2 Discontinuities with an aperture>0
This vector contains 215 samples with a discontinuity present and 1374 with no discontinuity present.
This gives a predictive capacity 86.5% or 1383/1598. As discussed in section 2.7.5, this high ratio might
affect the outcome of the Hosmer and Lemeshow’s test.
In total SPSS generated 5 possible combinations of explanatory variable for which the condition of the
Wald statistic is met. All show to be improving the model without explanatory variable according to the
Omnibus Test of Model Coefficients. For none of these combinations, the data set is found to fit the
logistic regression model, according to the Hosmer and Lemeshow’s test. The pseudo 𝑟2 value are
below 0.168. Regardless if the Hosmer and Lemeshow’s test is effected, the pseudo 𝑟2 value are to low
for an useful model. The best model predicts 28 of the 218 samples with a discontinuity present.
5.4.1.3 Discontinuities with an aperture>0 and no infill
There are 19 sampling points with an open non-infilled discontinuity and 1570 sampling points with no
discontinuity. This ratio might affect the outcome of the Hosmer and Lemeshow’s test.
SPSS generated 5 models that meet the Wald statistics, Omnibus Test of Model Coefficients and
Hosmer and Lemeshow’s test. From the 19 sampling points, two models predict 4 sampling points with
a discontinuity correct. The other models predict 2 or 3 sampling points correct. This model has a
pseudo 𝑟2 value of 0.539.
The results of the regression analysis of these two models is given in appendix 11. The model in step 5
is the best model. The regression equation of step 5 is given below.
𝑝 =exp (−7.870−2.552𝑃𝐶2−0.674𝑃𝐶3+0.692𝑃𝐶4+2.086𝑃𝐶5+0.987𝑃𝐶6)
exp (−7.870−2.552𝑃𝐶2−0.674𝑃𝐶3+0.692𝑃𝐶4+2.086𝑃𝐶5+0.987𝑃𝐶6)+1 Regression Eq. 1
The standardized beta-weight are respectively -0.0382, 0.0081, 0.0083, 0.2895 and 0.0121. Principle
component 2 represents some of the raw MWD data, component 3 represents normalized penetration
and principle component 4 represents RMS normalized penetration. Raw and processed rotation
pressure is represented by component 5 and 6.
The model is subjected to a sensitivity test. Generated beta-weights by leaving out a random 10% of
the data do not deviate significantly form the original beta-weights. The results are given in Table 21.
When comparing the predicted probabilities with the discontinuity location vector in more detail, it is
found that the given model predicts 2 out of the 3 discontinuities with a small shift in location. Both
fractures in borehole 5 are detected. The fracture at a depth of 3.5m is represented in the discontinuity
location vector with 8 samples. The regression equation predicts 4 samples of which 2 are shifted 4cm
out of the defined location. The fracture at a depth of 4.6m is defined with 3 samples. The regression
equation predicted 4 samples of which again 2 are shifted 4cm out of the defined location. This is
visualized in appendix 15. Although the pseudo 𝑟2 value for this regression equation is not high, a fair
part of the fractures in the training set is predicted.
65
Table 21. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and no infill.
Principle components
Original Generated for sensitivity analysis
Beta-weight Average beta-weight Standard deviation beta-weight
2 -2.552 -2.929 0.673518
3 -0.674 -0.816 0.211748
4 0.692 0.747 0.054972
5 2.086 2.591 0.726557
6 0.987 1.034 0.086609
Constant -7.870 -8.716 1.155729
5.4.2 Training dataset 2
In appendix 10 the correlation coefficients of the discontinuity location vectors and principle
components are given. Compared to dataset 1, there are higher and more significant coefficients. It can
be expected that regression equations predict locations of discontinuities better. The relevant SPSS
outcome can be found in appendix 12.
5.4.2.1 All discontinuities
The discontinuity location vector contains 755 samples of which 283 with a discontinuity. This ratio is
the recommended ratio for the use of the Hosmer and Lemeshow test.
SPSS generates two models for which the Wald statistics are met. One of the models fits the data
according to the Hosmer and Lemeshow test. I has pseudo 𝑟2 value of 0.94 and predicts only 40 out of
the 283 samples with a discontinuity correct.
5.4.2.2 Discontinuities with an aperture>0
There are 141 out of 755 samples which represent discontinuities with an aperture. The ratio is close
to the recommended 1 to 4 ratio for the use of the Hosmer and Lemeshow test.
From the 4 models SPSS generates, 3 models fit the data according to the Hosmer and Lemeshow test.
One has a pseudo 𝑟2 value of 0.471. It predicts 45.4% of the samples with a discontinuity correct. The
regression equation corresponding to this model is given below.
𝑝 =𝑒𝑥𝑝 (−2.247+1.102𝑃𝐶3−1.526𝑃𝐶4−1.008𝑃𝐶5)
𝑒𝑥𝑝 (−2.247+1.102𝑃𝐶3−1.526𝑃𝐶4−1.008𝑃𝐶5)+1 Regression Eq. 2
The model is not sensitive to deleting data. The results of the sensitivity analysis are given in Table 22.
Table 22. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0.
Principle components
Original Generated for sensitivity analysis
Beta-weight Average beta-weight Standard deviation beta-weight
3 1.102 1.108 0.03052
4 -1.526 -1.485 0.05400
5 -1.008 -1.006 0.04596
Constant -2.247 -2.249 0.04650
These principle components represent raw MWD data, some of the processed water parameters and
normalized penetration. The calcite veins at -1.1m and -0.7m in borehole 1, at 0.2m in borehole 2 and
the fracture at 3.5m in borehole 5 are predicted by the regression equation. The first and last encounter
of the upper and lower boundaries of the discontinuities are predicted maximum 4cm of the defined
location. Other discontinuities are not predicted or predicted with only a couple of samples.
66
5.4.2.3 Discontinuities with an aperture>0 and no infill
Out of 755 samples, 20 are representing fracture locations. This ratio might affect the Hosmer and
Lemeshow test.
All 5 models which SPSS generates met the Wald statistics and Hosmer and Lemeshow test. The best
model has a pseudo 𝑟2 value of 0.631. It predicts just over half the samples correct. The regression
equation is given below.
𝑝 =
𝑒𝑥𝑝 (−8.252−0.941𝑃𝐶1+0.980𝑃𝐶2
−3.297𝑃𝐶3−1.207𝑃𝐶4+1.793𝑃𝐶5)𝑒𝑥𝑝 (−8.252−0.941𝑃𝐶1+0.980𝑃𝐶2
−3.297𝑃𝐶3−1.207𝑃𝐶4+1.793𝑃𝐶5)+1
Regression Eq. 3
The average and standard deviation of a sensitivity analysis is given in Table 23.
Table 23. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and no infill.
Principle components
Original Generated for sensitivity analysis
Beta-weight Average beta-weight Standard deviation beta-weight
1 -0.941 -0.943 0.052515
2 0.980 1.024 0.017099
3 -3.297 -3.447 0.20961
4 -1.207 -1.150 0.062708
5 1.793 1.654 0.137164
Constant -8.252 -8.301 0.213749
When studying the predicted probabilities in more detail, it turns out that the fracture at 3.5m in
borehole 5 and the fracture at 3.3m in borehole 6 are predicted. For both the size of the predicted
fracture is 4cm smaller than the defined fracture. This is visualized in appendix 15.
5.4.3 Training dataset 3
The Pearson’s correlations between discontinuity location vector and principle components are given
in appendix 10. The correlations are low. The principle components representing normalized water
parameters, RMS normalized penetration and RMS normalized rotation pressure seem to be best
correlating with discontinuity vectors. The relevant SPSS outcome can be found in appendix 13.
5.4.3.1 All discontinuities
There are 296 samples which are defined as discontinuities. With a total of 1302 samples, the ratio
between the two cases is good for the Hosmer and Lemeshow test.
All 4 generated models by SPSS met the Wald statistics and Hosmer and Lemeshow test. The pseudo
𝑟2 values are between 0.13 and 0.141. At the most, 14.9% of the samples with discontinuities is
predicted correct. All of the predictions are in boreholes 21, 22 and 24.
5.4.3.2 Discontinuities with an aperture>0
The regression models generated for the discontinuity groups with an aperture, soft infill and no infill,
are predicting less than 10% of the defined discontinuity locations.
5.4.3.3 Discontinuities with an aperture>0 and no infill
The ratio between samples with and without discontinuities present is 49 to 1253. This ratio might
affect the Hosmer and Lemeshow test.
67
SPSS generates 4 models which meet the Wald statistics. The Hosmer and Lemeshow test indicates that
3 regressions fit the data. The pseudo 𝑟2 value are between 0.23 and 0.251. The regression equation is
given below. Normalized water parameters and normalized penetration are not included in the
regression model.
𝑝 =exp (−3.812−0.438𝑃𝐶2+0.569𝑃𝐶4+0.517𝑃𝐶5−0.555𝑃𝐶6)
exp (−3.812−0.438𝑃𝐶2+0.569𝑃𝐶4+0.517𝑃𝐶5−0.555𝑃𝐶6)+1 Regression Eq. 4
The model is subjected to a sensitivity analysis. By deleting a different 10% of the data 5 times, the
average and standard deviation is calculated. The results are displayed in Table 24, the model is not
sensitive.
This regression predicts only 6 out of the 49 samples which represent a discontinuity. It is only covering
the fracture at 0.65m in borehole 24.
Table 24. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and no infill.
Principle components
Original Generated for sensitivity analysis
Beta-weight Average beta-weight Standard deviation beta-weight
2 -.438 -.396 0.034311
4 .569 .559 0.032667
5 .517 .528 0.059723
6 -.555 -.566 0.050331
Constant -3.812 -3.821 0.071536
5.4.4 Training dataset 4
Like with the correlation coefficients of dataset 1 and dataset 2, there are better correlations with the
large hole dataset 4 than with normal hole dataset 3. Processed penetration rate, represented by
principle components 1 and 6, are highest correlated with discontinuity vectors. In appendix 10 the
correlations can be found. The relevant SPSS outcome can be found in appendix 14.
5.4.4.1 Discontinuities and with an aperture>0
The ratio samples with or without discontinuities is close to the recommended ratio. The Hosmer and
Lemeshow test does not indicate that the discontinuity location vector including all discontinuities fits
the logistic regression model. When considering discontinuities with an aperture>0, there is one model
which meets the significance level of the Hosmer and Lemeshow test. The pseudo 𝑟2 value 0.104 and
only 7.8% of the samples with a discontinuity is predicted correctly
5.4.4.2 Discontinuities with an aperture>0 and soft or no infill
There are 118 samples representing a discontinuity. With the in total 1531 samples, the ratio between
samples with and without discontinuities might affect the Hosmer and Lemeshow test.
SPSS generated 4 models which meet the Wald statistic. Three of the discontinuity vectors seem to fit
the logistic regression. The highest pseudo 𝑟2 value is 0.425. This model predicts 33 of the 118 samples
with a discontinuity correct. The regression equation is given below.
𝑝 =exp (−3.513+1.036𝑃𝐶1+0.312𝑃𝐶3+0.441𝑃𝐶5+0.748𝑃𝐶6)
exp (−3.513+1.036𝑃𝐶1+0.312𝑃𝐶3+0.441𝑃𝐶5+0.748𝑃𝐶6)+1 Regression Eq. 5
In Table 25the results of a sensitivity analysis are given. There are no large differences with the original
model.
68
Table 25. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and soft or no infill.
Principle components
Original Generated for sensitivity analysis
Beta-weight Average beta-weight Standard deviation beta-weight
1 1.036 1.068 0.009751
3 .312 0.320 0.043779
5 .441 0.443 0.043597
6 .748 0.753 0.010833
Constant -3.513 -3.564 0.028872
None of the fracture in boreholes 11 to 20 are predicted. Form the in total 9 fractures with soft infill in
boreholes 21, 22 and 24, 6 are predicted with small deviations in size and location. For the fractures
with no infill 2 out 4 for are predicted. This is visualized in appendix 15. Even though the pseudo 𝑟2
value is not high, a fair part of the discontinuities with a soft infill are predicted.
5.4.4.3 Discontinuities with an aperture>0 and no infill
The ratio samples with and without a fracture is 70 to 1498. This ratio deviates significantly form the
recommended ratio and it might influence the outcome the Hosmer and Lemeshow test.
Out of the 3 models SPSS generates, 1 model meets both Wald statistics and the Hosmer and Lemeshow
test. The pseudo 𝑟2 value is 0.281 and only 9 out of 70 samples is predicted correctly. When studied
with more detail, it turns out that only the fracture at 1m in borehole 22 is predicted. The regression
equation is given below.
𝑝 =exp (−3.810+0.768𝑃𝐶1+0.622𝑃𝐶5+0.316𝑃𝐶6)
exp (−3.810+0.768𝑃𝐶1+0.622𝑃𝐶5+0.316𝑃𝐶6)+1 Regression Eq. 6
The model is not sensitive to deleting data. The results are given in Table 26.
Table 26. Outcome of sensitivity analysis of regression analysis with discontinuities with an aperture >0 and no infill.
Principle components
Original Generated for sensitivity analysis
Beta-weight Average beta-weight Standard deviation beta-weight
1 .768 0.814 0.037938
5 .622 0.624 0.044856
6 .316 0.316 0.047984
Constant -3.810 -3.862 0.064417
5.4.5 Summary of logistic regression analyses
Training of the different datasets leads to multiple models which meet the Wald statistics and Hosmer
and Lemeshow test. The quality of the model is studied by looking at predictions of input fractures. For
each attempt to predict a certain type of discontinuity that meets the Wald statistics and Hosmer and
Lemeshow test, the best predicting equation is given in the previous sections. These models are
summarized in Table 27.
69
Table 27. Overview of the best predicting models which meet the Wald statistics and Hosmer and Lemeshow test.
Equation Discontinuity type
Equation Data Prediction
1 Open fracture
All principle component except PC 1, which represents normalized rotation pressure
1570 sampling points of which 19 represent an open fracture
2 out of 3 fractures are predicted with a small shift in locations
2 Discontinuities with an aperture>0
3 principle components representing processed water parameter, raw parameters and normalized penetration
755 sampling points of which 141 have a discontinuity with an aperture
4 out of calcite veins are predicted with a maximum shift in location of 4cm
3 Open fractures All principle components.
755 sampling points of which 20 have an open fracture
The two largest open fractures are predicted
4 Open fractures 4 principle components representing Normalized rotation pressure and RMS processed data
1253 sampling points of which 49 represent an open fracture
Only 6 of the 49 sampling points are predicted correctly
5 Fractures with a soft or no infill
4 principle components which represent all processed penetration and rotation pressure
1531 samples of which 118 samples represent an open or filled fracture
6 of the 9 fractures with soft infill are predicted with a small deviation in size and location Open fractures were not predicted
6 Open fractures 3 principle components representing normalized penetration and RMS normalized penetration and rotation pressure
1498 sampling points of which 70 represent a fracture
Only 9 of the 70 sampling points are predicted correctly
5.5 Regression equation
Datasets 5 to 8 are reserved to test the regression equations found in the previous subchapter. In this
subchapter, principle components are calculated for each dataset by use of the corresponding
component score coefficient matrix in appendix 8. Next, the regression outcome of dataset 1 is tested
with corresponding dataset 5. The same counts for dataset 2 and 6, etc. The test datasets do not contain
data used for principle component analysis, cluster analysis or regression analysis.
5.5.1 Testing regression outcome from dataset 1
As mentioned in section 5.1, dataset 1 does not include boreholes 9 and 10. For boreholes 9 and 10 is
was not possible to find an appropriate shift. Regression equation 1 is supposed to predict the present
of open fractures. When testing dataset 5, the expected outcome is to find no open fracture.
The predicted locations of an open fracture are at 4.74-4.82m in borehole 9, at 0.76m and 4.86m in
borehole 10. Except for a peak in penetration rate at 0.76m in borehole 10, there are no causes found
for these expected locations. There are no open fractures present in dataset 5.
70
5.5.2 Testing regression outcome from dataset 2
Dataset 6 consists of boreholes 3, 4 and 7 to 10. Aligning the televiewer and MWD depth gave an
uncertainty which is the reason they are not included in dataset 2. Regression equation 2 is generated
with discontinuities with an aperture>0 as input. Regression equation 3 should predict open fractures.
In borehole 4 a calcite vein at 3.68m deep is predicted correctly. Next to this prediction none of the 5
discontinuities with an aperture is predicted. Drops in penetration rate, hammer-, feeder- and rotation
pressure between led 10 times to a prediction of a discontinuity while these responses are not due to
geology. The remaining 3 predictions of a discontinuity cannot be related to certain responses in MWD
data.
The 6 predictions made by regression equation 3 are all related to the same drops in raw MWD data as
discussed above. In dataset 6 there are no open fractures present.
5.5.3 Testing regression outcome from dataset 3
Dataset 7 consists of boreholes 16, 17, 23 and 25. There is only an unprocessed televiewer image
available of borehole 16. The depth registration of borehole 17 is not correct and cannot be shifted.
Borehole 23 showed an extremely low penetration rate and the MWD data of borehole 24 has been
merged out of two MWD files. Regression equation 4 is supposed to predict open fractures. There is at
least one open fracture present in borehole 23.
Regression equation 4 does not predict any open fractures in dataset 7.
5.5.4 Testing regression outcome from dataset 4
Dataset 8 includes 6 discontinuities with soft infill and at least one open fracture. Regression equation
5 and 6 are supposed to predict discontinuities with soft infill and open fractures. Dataset 8 consists of
boreholes 16, 17, 23 and 25.
None of the named discontinuities is predicted. In total, equation 5 and 6 predict 4 discontinuities
where there is a drop in raw MWD data.
5.5.5 Overview of predicted discontinuities
In Table 28, an overview is given of the correct, incorrect and present discontinuities in the test datasets
5, 6, 7 and 8.
Table 28. Overview of predictions in the test datasets.
Testing dataset
Equation Discontinuities to be predicted
Correct predicted discontinuities
Incorrect predicted discontinuities
5 1 No open fractures - 7 sampling intervals
6 2 5 discontinuities with an aperture
1 calcite vein is predicted
3 No open fractures - 6 sampling intervals
7 4 1 open fracture - -
8 5 6 fracture with soft infill and 1 open fracture
- 4 discontinuities
6 1 open fracture - 4 discontinuities
71
6. Discussion
The goal of this study is to investigate the ability to detect discontinuities and their geometrical
properties in MWD data. Furthermore, this study aims to evaluate the usability of MWD data in terms
of detecting discontinuities for Norwegian Public Roads Administration as a client. To achieve this goal,
a literature review, fieldwork, visual comparisons and statistical comparisons of MWD data have been
carried out. This chapter summarizes the most important findings and potential implications.
6.1 Literature study
In past studies, the mismatch between scale in geological mapping and MWD data led to complications
when comparing geology with MWD data. In order to study the response of MWD data to individual
discontinuities, a much more detailed method than the regular geological mapping by engineering
geologists was needed.
Whereas normal users of MWD software interpret the drilling parameters without utilizing geological
data, this study aims to characterize responses to discontinuities based on detailed geological data. In
order to get a more in depth understanding and confirmation of visual characteristics, two
unsupervised statistical methods and one supervised statistical method have been found suitable for
MWD and geological data. Principle component analysis was presumed to reduce the dimension of the
MWD datasets. K-means cluster analysis was presumed to identify clustering in the data. Logistic
regression was presumed to be useful for training and testing of predictive equations. For this study,
the geological translation to statistical input had no continuous nature. The presence or absence of a
discontinuity gave a binary input and logistic regression was used to study the relation between
geological input and MWD data. It was found that a linear regression is not capable of tackling binary
independent variables and is restricted by additional assumptions.
6.2 Data gathering and collected data
Knowing where a certain fracture is located in a tunnel and finding the corresponding MWD data was
the major challenge. Mapping geology in a tunnel under construction is complicated a range of factors.
Artificial light, reachability and safety influenced the quality of the geological data collected.
Mapping discontinuities in a borehole remain is a good method if MWD data and geological records
can be aligned. Without borehole remains, one is restricted to approximate the position of larger
discontinuities. The aperture and nature of infill are important geometrical properties since these
influence the magnitude of response to discontinuities. Without doubt, records from televiewer
inspections are the most detailed and useful data collected in this study. This method captures rock
masses which are least influenced by blasting. Geological data gathered by mapping the tunnel contour
and filming boreholes with an inspection camera did not result in data detailed enough for statistical
analysis.
The majority of the collected data was appropriate for a visual comparison. The data collected with the
televiewer was also appropriate for a statistical analysis.
6.3 Visual validity assessment of MWD data
Comparing the mapped geology and 3D images of MWD data revealed that fractures with a certain infill
or aperture are visible in MWD data. Even though not all holes showed the presence of a fracture,
enough holes showed the location of a fracture so that their positions and orientation could be
extracted.
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The geological record of a borehole remain allowed for a detailed comparison of discontinuities and
MWD data. It gave a clear response for clay filled fractures. Other fractures occasionally gave a
response.
Studying the correlation between MWD data and discontinuities in highly foliated phyllite did not
provide good results. Thus, it can be stated that a study like this should not be aimed on poor rock
masses.
It was found that for the Bjørnegårdtunnel, the parameter RMS normalized penetration gives the
clearest response to fractures with a moving average of 4 values.
The visual comparison of MWD data and the televiewer recordings led to clear responses to open
fractures and fractures with soft infill. The following responses were observed for open fractures or
fractures with soft infill with a dip angle larger than 62° and an aperture more than 1cm:
- The penetration rate peaks.
- The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture is
encountered.
- The feeder pressure drops.
- The rotation pressure peaks.
- The normal hole water flow drops and then increases gradually.
- The processed penetration and rotation pressure peak.
Sometimes the peaks were followed by a short small drop before the parameter could reach its old
level. In MWD data of large hole drilling, the hammer pressure often dropped when encountering a
fracture. Fractures with dip angles smaller than 62 degrees and with an aperture smaller than 1cm were
not observed. The magnitude of the responses were found to be different for each fracture and no
pattern in magnitude between open and filled fractures was found. This is in contrast with the findings
listed in Schunnesson thesis (section 2.4). Sometimes the peaks were followed by a short small drop
before the parameter could reach its old level.
A point worth discussing is the gathering of geological data. Gathering more detailed geological data
has shown to contribute to the understanding of MWD data and fracturing. It can however be argued
that only a small amount of data was mapped at the tunnel face and contour. It should be emphasized
that the accuracy of geological data from contour mapping is highly dependent on the rock mass quality
and on drill and blast activities. Another aspect, which should be noted is that there are other rock
properties besides fracturing, which could influence the processed MWD data. To study the actual
response of discontinuities, it is preferred that MWD data is not influenced by other factors than
fracturing. Since the studied rock masses are influenced by other factors than fracturing, this has led to
the evaluation of geological conditions, which are experienced in real life excavations. This suits the aim
of evaluating the practical applications of MWD technology for the Norwegian Public Roads
Administration.
6.4 Statistical validity assessment of MWD data
From visual comparison, it is suspected that not more than the actual location and aperture can be
predicted. Orientation did not seem predictable based on a single hole. It was chosen to assign the
number 1 to each MWD sampling depth between the upper and lower boundary of a discontinuity. For
intact rock, a 0 is assigned to each MWD sampling depth. For regressions including only the more
predictable discontinuities like fractures, the fractures are assigned a 1. Other discontinuities are
assigned a 0.
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The principle component analyses showed that the dataset could be reduced to a manageable number
of 5 to 7 components. Two of the datasets were nicely reduced to 6 components, where each
component contained one type of processed parameters and their raw parameters. All principle
component analyses showed high communalities, which indicates that high proportions of each
variable's variance were preserved after the principle component analysis. Close to 80% of the total
variance was explained by the principle components of each dataset. PCA was found to be an
appropriate analysis for MWD data gathered for this thesis.
K-means cluster analyses was found to be partly valuable 2 out of 4 datasets. It led to the understanding
that responses in MWD data due to lithological discontinuities, which have no aperture, cannot be
separated from intact rock. A fair part of fractures with no infill were separated by clusters, which
indicates that the MWD responses at the location of these fracture differ significantly from MWD
responses elsewhere.
Training the data with logistic regression analyses confirmed that responses for lithological boundaries
cannot be differentiated from the MWD data of intact rock. Logistic regression did however
differentiate between MWD data from open fractures and other MWD data in dataset 1 and 2. 2 out
of 3 fractures where predicted. These regression equations have pseudo 𝑟2 values of 0.539 and 0.631,
which is not particularly high. This is due to fact that the exact location and size of predicted fractures
differed slightly from the input locations. Furthermore, as stated in section 2.7.4, the sample number
can have great influence on the outcome of logistic regression. All datasets have a high enough number
of samples. However, the number of samples which are assigned a 1 due to presence of a discontinuity
with infill or open fractures is low. As mentioned in section 2.7.4, for the Hosmer and Lemeshow test a
ratio of 0.25/0.75 is recommended. The ratio of the predictive models described is often lower. For
dataset 3 and 4, the prediction of open fractures did not provide any conclusive results. The regression
with the fractures with soft infill was successful for dataset 4. 6 out of 9 fractures with clayey infill were
predicted with a small deviation in size and actual location. One regression led to the prediction of
45.4% of the samples with calcite infill.
Testing the regression equations on test datasets, which were not part of the input for data training,
did not lead to the correct prediction of fractures. A reason for this might be that the MWD data in the
test datasets was less appropriate as input for a regression analysis. Extremely late drilling on full speed,
uncommon drilling behaviour or data without a clear marker were a part of the test datasets. In
addition, the number of fractures and amount of data was not too much.
Another point of discussion is the translation of geological data to statistical input. With translating the
depths of discontinuities to discontinuity location vectors, it was assumed that the response to a
fracture is between the boundaries of the fractures. In reality, responses in MWD data are not bounded
by the actual location of discontinuities. As observed in this study, occasionally the water flow and
hammer pressure dropped when drilling through a fracture. These parameters did however reach their
old level long after the fracture had been drilled through. Furthermore, it was assumed that one
response takes place over the whole interval where a discontinuity is present. It was observed that
parameters occasionally showed a peak followed by a small drop within the boundaries of a fracture. It
might well be that this has influenced the outcome of regression. Another aspect, which might have
influenced the outcome of the statistical analyses is the fact that processed data involves, among other
operations, averaging of values ahead of the computed value. This leads to premature responses when
considering the actual location of a fracture
Ultimately, despite being limited by its apparent inability to predict each type of discontinuity, MWD
technology has great potential within the field. It can be questioned if the used statistical analyses are
74
appropriate methods to study this data since the outcome of the statistical analyses are not
overwhelming. The discussed assumptions considering vectorisation of geological data and the
sampling interval might however be the main cause for the moderately convincing outcome of the
statistical analyses. If in the future both knowledge of data processing expands and data flow develops
with IT’s developments, it is believed that MWD data can definitely be useful in terms of fracture
recognition.
6.5 Current usability of MWD
Considering applications of MWD technology in terms of fractures, using MWD data for predicting rock
quality designation would lead to a severe underestimation of the RQD. The RQD value for the
Bjørnegårdtunnel is mainly dependent on lithological discontinuities, which could not be identified. This
leads to the conclusion that using MWD data for Q-system evaluation is not possible with today’s MWD
technology. Another aspect of tunnelling where the usability of MWD technology is being studied is
understanding and predicting grout volumes. Since grout seals fractures, the findings in this study could
contribute to research in grouting volumes. A final application, which showed to be useful for fracture
detection are 3D images of MWD data. Whilst a prediction of a single fracture cannot help to estimate
the orientation of a fracture or to understand the structural implications of excavation, a 3D image of
MWD data of a blast round with multiple detections can help. As shown in this thesis, 3D images
showing fractures with a certain aperture detected in multiple holes gave a good understanding of the
in situ fracture structure, even if responses to fractures were not present in all holes. This knowledge
can be used to anticipate possible under- and overbreak results due to these fractures before blasting.
As a precaution, one could achieve a better contour by reducing blast round length or adjusting the
blast design. In terms of rock support, MWD data can assist in the decision making concerning spot
bolting to secure wedges due to large fractures.
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7. Conclusion and recommendations
This chapter summarizes the most important findings and conclusions, provides answers to research
questions presented in this study and gives recommendations for similar research in the future.
Firstly, it can be concluded that the more detailed geological information collected for this thesis gives
much better insight into the detection of discontinuities and its potential than found in reviewed
studies. Geological data collected with the optical televiewer in particular provides useful data to gain
insight into responses to discontinuities. Based on the visual comparison, it can be stated that open
fractures or fractures with a soft infill give a consistent characteristic response. In 3D images of MWD
data, the orientation of these fractures can be obtained. In practice, this contributes in decision making
in spot bolting and anticipating possible under- and overbreak. The principle component analysis is an
useful analysis for MWD data. It showed the ability to reduce datasets significantly and at the same
time retain a high level of variability. However, the question still remains whether a k-means cluster
analysis is an appropriate method for analysing clustering in MWD data. Logistic regression occasionally
showed the possibility to detect the location and aperture of open fractures and fractures with a soft
infill, which were part of the input data. Finally, it can be concluded that there is no characteristic
difference in responses between lithological discontinuities and intact rock or between zones with low
and high discontinuity spacing.
The main research question is: To what extent can discontinuities be detected by MWD data and used
for reporting geology in a tunnel, predicting geology ahead of the tunnel face and predicting the amount
of rock support needed? Based on the whole thesis, this question can be answered as follows. With
today’s MWD technology, discontinuity detecting and its applications is limited to fractures with a
certain aperture and infill. Nonetheless, the potential of MWD technology remains. The detection of
open and infilled fractures gives multiple applications and could open doors for research in different
fields. If in the future both knowledge of data processing expands and data flow develops with IT’s
developments, it is believed that MWD data can definitely be useful in terms of fracture recognition.
Research questions
The first objective was to conduct a literature study on the subjects MWD technology, discontinuities in rock masses, statistical analysis that can be used to evaluate MWD data and previous research on the subject of MWD validity.
- What is MWD technology in drill and blast tunnelling? The utilisation of raw and processed drilling parameters to gain knowledge about the rock mass quality in terms of hardness, fracturing and water conditions.
- What kind of discontinuities can be encountered? Discontinuities that can be encountered are lithological discontinuities, foliation, fractures, faults and shear zones. For this project, blast induced fractures are also relevant.
- How is geology mapped in tunnels by the Norwegian Public Roads Administration? The engineering geologist documents the input for the Q-system, classifies rock type and draws large fractures in an illustration of an unfolded tunnel contour. This is done for every drill and blast cycle.
- How is MWD data filtered and processed? The processing of MWD data involves a series of steps which filter out influences like drill bit wear, percussive pressure and depth dependency. The result is a modified penetration rate, torque and water pressure where fluctuation intensity is suppressed and intensified by a moving average. This outcome is currently used to interpret rock mass in terms of hardness, fracturing and water conditions.
76
- What statistical analyses are appropriate for analysing MWD data to enhance and understand the predictability of discontinuities?
Three statistical analyses are found to be useful in order to get a better understanding of MWD data and predict locations of discontinuities namely, Principle Component Analysis, k-means cluster analysis and multiple logistic regression.
- What is the result of previous research on the validity and usability of MWD? In previous research, it is found that the hardness parameter correlates well with geological boundaries and there is a visible relationship between MWD water conditions and fracturing with respect to the grouting volumes. RQD is found to correlate moderately with MWD fracturing number. Schmidt hammer hardness has not been shown to correlate with the MWD hardness number. The second objective was to gather data, which is appropriate to study relations between MWD data and discontinuities.
- What is the local geology of the tunnel sites? At the Bjørnegårdtunnel site, sedimentary rocks are folded and tilted towards extensional faults of the partial rifted Oslo field. The rock types excavated at the Solbakktunnel are Precambrian gneiss and highly foliated phyllite.
- What methods can be used to map discontinuities in tunnels? Methods to map discontinuities are mapping the tunnel face and contour, mapping borehole remains and inspecting boreholes with an inspection camera or optical televiewer.
- What are the parameters to obtain during fieldwork? The most important and difficult information to obtain is the location of a discontinuity with respect to MWD data. In addition, the aperture and nature of infill are important. Finally, the parameters to obtain are overall RQD, Schmidt hammer strength of discontinuity planes and intact rock, joint set, orientation, persistence, weathering state of discontinuity plane, joint roughness and joint alteration number.
- To what extent is it possible to work on sites where the degree of fracturing is the only variable parameter?
There were other rock properties, besides fracturing, which could influence the processed MWD data. Finding locations where the degree of fracturing is the only variable is very difficult.
- What is the quality of the collected data? The data gathered with the optical televiewer is detailed enough for a visual comparison and statistical analyses. The data gathered by mapping geology at the tunnel face is only useful for a visual comparison of MWD data and larger discontinuities. Artificial light, reachability and safety reduced the level of detail gathered from mapping geology, which makes statistical analyses of data not possible. The data collected with the inspection camera is less useful due to intense foliation and absence of other discontinuities. The third objective was to evaluate the data by visual comparisons of geological data collected during fieldwork and MWD data.
- What kind of discontinuities are visible in MWD data or what kind of geometrical properties make discontinuities visible in MWD data?
Fractures with an aperture of a couple of centimetres are visible in MWD data. Infill which is much softer than the rock or no infill give a clearer response. Aperture and nature of infill are the most important properties.
- Is there a characteristic response of MWD data to discontinuities? Open fractures or fractures with a soft infill give multiple or all of the following responses in MWD data:
1. The penetration rate peaks.
2. The large hole hammer pressure drops and increases about 0.2 to 0.25m after the fracture
is encountered.
77
3. The feeder pressure drops.
4. The rotation pressure peaks.
5. The normal hole water flow drops and increases gradually.
6. The processed penetration and rotation pressure peak. The magnitude of the responses are found to be different for each fracture and no pattern in magnitude between open and filled fractures is found. This is in contrast with the findings listed in Schunnesson thesis (section 2.4). The fourth objective was to apply statistical methods to come to a more in depth understanding of the relation between MWD data and discontinuities and confirm findings in objective 3.
- What geological data can be used for statistical analyses? Geological data gathered with the optical televiewer can be used for a statistical analysis if the depth of geological recordings and MWD can be aligned.
- How should geological data be translated to statistical input? Considering a single hole, the information that could be predicted is location, aperture and possibly nature of infill. This information needs to be translated to the same depth scale as MWD data. It is chosen to assign a 1 to the sampling points where a discontinuity is present and 0 zero elsewhere.
- Are the statistical analyses appropriate for the MWD data collected? Principle component analysis is useful. The datasets are reduced to a manageable number of components. K-means cluster analyses was found to be an appropriate analysis for 2 out of 4 datasets. Regression analysis was found to be useful for detecting fractures with an aperture larger than 1 cm and soft or no infill.
- Can discontinuities and/or certain geometrical properties be predicted by use of statistical methods?
Regression analysis showed that open fractures and fractures with a soft infill can be partly predicted with a small deviation in depth and aperture with respect to the input data. The last objective will be to discuss the current and future usability of MWD data for the Norwegian Public Roads Administration.
- To what extent are discontinuities visible in MWD data? Fractures with a certain aperture and soft or no infill give a characteristic response. There is no characteristic difference in responses for lithological discontinuities and intact rock. There was no clear difference in response in MWD data between zones with low and high discontinuity spacing.
- In which way could the outcome of this study be used to improve the process of drill and blast tunnelling?
Although the focus of this study has not been to investigate how to improve drill and blast tunnelling, the outcome of this study could potentially be used in two ways. Multiple large fractures in one blast round might give under- and overbreak. By detecting these fractures before blasting, measure can be taken. Another field where the outcome might be of interest is the research in grouting volumes. Grouting volumes are partially dependent on fracturing in rock and the detection of fractures with an aperture could contribute to research in grouting volumes.
- What significance do detected discontinuities have for rock support determination? Since lithological discontinuities contribute to the RQD but are detected by MWD data, interpreted
MWD data cannot be used to predict RQD. The detecting of fractures could for example help in decision
making for securing wedges with spot bolting.
Recommendations
Users of MWD software and services should be cautious with the interpretation of MWD data and
fracturing, especially when using sampling intervals much greater than the common 2 cm. An outcome
78
of MWD data defined as ‘fracturing’ or ‘fracturing index’ does not differ from a processed MWD
parameter as described in this thesis, even though the name of an outcome parameter might suggest
differently. Furthermore, MWD technology is believed to be useful as a support for mapping the
geology and not supposed to replace it.
An aspect that future researchers could invest in is the sampling interval of MWD data. Several attempts
to decrease the sampling interval to 1cm or 0.5cm did not lead to a smaller sampling interval of the
output data. Whether this information was lost in data flow or the systems on the drilling rig could not
get to a sampling interval smaller than 2cm is unknown. A smaller sampling size would allow researchers
to investigate discontinuities with smaller apertures, ultimately allowing a more precise study of MWD
responses.
As discussed in section 6.4, the number of samples representing a discontinuity was often low
compared to the total dataset. In order to improve the training of data and computing predictive
equations it is recommended to gain a high number of fractures in the data. Of course this is easier said
than done, but when planning to drill inspection holes this should be considered as a very important
point.
Another point discussed in section 6.4 is the reason for disappointing results from testing the regression
equations. For a future research it is recommended to work with a larger test dataset. This does not
have to be data gathered by inspection. On the other hand, this might be too expensive and time
consuming. Detailed mapped geology of the tunnel contour where boreholes are inspected would give
plenty of MWD data to test regression equations.
Considering the geological mapping in a tunnel to gain detailed information about discontinuities, 3
final recommendations are made. A borehole camera with a 90° view gives a much better view of
fractures than a camera with a forward aimed lens. If possible, it is recommended to map tunnel
contour a couple of sections behind the tunnel excavation. This way, there might be not too much
disturbance for researcher and contractor. Regarding the use of a televiewer, the depth measurements
can be increased by leaving as little space between the borehole and reel. This prevents wobbling of
the cable and incorrect depth measurement as described in section 4.2.12.
79
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