an integrated approach for the prediction of subsidence for coal mining basins
TRANSCRIPT
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An integrated approach for the prediction of subsidence for coal mining basins
Tugrul Unlu, Hakan Akcin, Ozgur Yilmaz
PII: S0013-7952(13)00232-9DOI: doi: 10.1016/j.enggeo.2013.07.014Reference: ENGEO 3648
To appear in: Engineering Geology
Received date: 17 November 2012Revised date: 18 July 2013Accepted date: 28 July 2013
Please cite this article as: Unlu, Tugrul, Akcin, Hakan, Yilmaz, Ozgur, An integratedapproach for the prediction of subsidence for coal mining basins, Engineering Geology(2013), doi: 10.1016/j.enggeo.2013.07.014
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AN INTEGRATED APPROACH FOR THE PREDICTION OF SUBSIDENCE FOR COAL
MINING BASINS
Tugrul UNLU* Hakan AKCIN Ozgur YILMAZ
[email protected] [email protected] [email protected]
Bülent Ecevit University,
Engineering Faculty, Mining
Eng. Dept. 67100,
Zonguldak,Turkey
Bülent Ecevit University,
Engineering Faculty, Geomatics
Eng. Dept. 67100,
Zonguldak,Turkey
Bülent Ecevit University,
Engineering Faculty, Mining
Eng. Dept. 67100,
Zonguldak,Turkey
+903722574010-1197
Fax: +903722574023
Abstract
In this study, land subsidence caused by underground mining activities was investigated by means of a
new subsidence prediction approach (ISP-Tech) which takes into account the most important parameters
contributing subsidence development such as coal production methods, depth, mining sequence and other
geomechanical characteristics of underground rock strata, etc. ISP-Tech can be applied to operating mines
to keep land subsidence under control as well as virgin coal sites to predict surface subsidence prior to
mining activities. In the method, geological information gathered from the Geographic Information
System (GIS) and the Mining Information System (MIS) are utilised to obtain geological cross-sections
which are used in finite element models for mesh building. Then, a number of two dimensional finite
element modelling analyses are carried out to determine land subsidence occurring due to mining
operations. Finally, land subsidence predicted from modelling studies is compared to the GPS and/or
Differential Interferometry Synthetic Aperture Radar (DIn-SAR) measurements. If incompatibility of the
results is detected, finite element meshes should be optimised, and then reanalysed to obtain more
compatible results. In the study, two different case studies were given as examples of the application of
ISP-Tech. Results of the case studies showed that ISP-Tech can successfully be applied to complex mine
subsidence problems. The proposed approach gives more accurate results than those obtained from other
classical subsidence prediction methods.
Keywords Subsidence; Coal Mining; MiningGIS; SAR Interferometry; Numerical Modelling; Mine
Production Map
*corresponding author
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1. Introduction
Subsidence due to mining activities begins with the excavation of ore from underground. Gravity and the
weight of the overlying rock strata result in the layers of rock to shift and sink downward into the goaf
area left by the extraction of coal seam. Therefore, this process can affect the surface, causing the ground
to sag and crack, which may damage surface structures (Fig.1). The extent and amount of subsidence due
to underground mining activities depend on a number of factors, such as mining depth, seam thickness,
overlying strata properties, production methods, panel dimensions, geological defects, surface topography
etc. Subsidence studies for coal mining areas initially originated in Europe in the middle of the last
century (Bauer, 2008). Since 1870 onwards, a number of scientific publications on subsidence studies
appeared in European countries and several alternative methods have been proposed to predict subsidence
parameters, including:
Graphical Methods, such as the National Coal Board Method used in the U.K.
Profile Function Methods
Influence Function Methods
Empirical Methods
Numerical Modelling Methods
Physical Modelling Methods
Profile function method seeks to define the shape of the subsidence profile using a single mathematical
formula. Therefore, it is generally only applicable to single panels, since it assumes the profiles to be
symmetrical and fails to recognise the way in which subsidence profile shapes are modified over adjacent
and previous longwall goaf areas. Influence function methods predict subsidence profiles based on the
theory of an area of influence around a point of extraction (Whittaker and Reddish, 1989). These methods
can be applied to a wide range of mining geometries, but are more difficult to calibrate and check than
profile function methods. Empirical methods can be developed for the prediction of subsidence
parameters whenever a large database of measured subsidence parameters is available. Numerical
modelling techniques have been developed in recent years using finite element and discrete element
models such as Phase-2D, UDEC etc.
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Ground subsidence due to underground coal mining is a major concern to the mining industry,
government and people affected. It is particularly of importance where mining activities take place under
urban areas. In Turkey, increasing population in mining areas brings about accommodation problems and
therefore unplanned urbanisation which constrains mining operations and coal production (i.e. requires
large pillars to protect surface structures). On the other hand, mining activities that take place under water
bearing basins such as lakes and sea may endanger safety and economy of the operations. Therefore,
prediction and measurement of the amount and the extent of subsidence are vital and preventive measures
should be taken to reduce risk and mitigate possible hazards. In most cases, classical subsidence
prediction methods used for the prediction of mine subsidence is lacking due to their limitations.
The aim of this study is to introduce a new subsidence prediction approach that can yield more reliable
results than those obtained from classical subsidence prediction methods. ISP-Tech can be applied to
working mines to keep land subsidence under control as well as virgin coal sites to predict surface
subsidence prior to mining activities. In the study, the use of two dimensional finite element modelling
technique, Mining Information System (MIS), Geographical Information System (GIS) and Differential
Interferometry Synthetic Aperture Radar (DIn-SAR) for the prediction and measurement of surface
subsidence over underground mine areas were presented. Two different case studies were given as
examples for the use of proposed approach.
2. Description of proposed approach
If a single coal seam or a coal panel is worked out, surface displacements and deformations can be
estimated by using one of the aforementioned classical subsidence prediction methods. However, it is
almost impossible to use the classical methods for the prediction of surface subsidence when underground
mining excavations take place in various coal seams at different depths simultaneously. Therefore, a new
subsidence prediction approach based on the application of two-dimensional finite element numerical
analysis on a specific number of geologic cross-sections gathered from Geological Information System
(GIS) and Mining Information System (MIS) was proposed. GIS and MIS are important parts of the
suggested approach, since different types of images, maps and spatial data (i.e., geological cross sections,
geological maps, drill hole data, etc.) can be utilised in numerical modelling studies (Fig. 2 and Fig 3).
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For the assessment of surface subsidence, two-dimensional elasto-plastic (E-P) stress analysis technique
(Phase2, ver. 8.0) has been used in modelling studies (Rocsience, 2012). In the two-dimensional E-P
stress analyses, the Hoek-Brown empirical failure criterion (Hoek and Brown, 1980) is considered for the
characterisation of the rock mass and coal strength. Its generalised version is expressed as (Hoek et al.,
1995; Hoek and Brown, 1997):
'max='min+ci(mb'min/ci+s)a (1)
where 'max and 'min are the maximum and the minimum principal effective stresses at failure,
respectively, ci is the uniaxial compressive strength of intact rock. Hoek-Brown constants “mb”, “s” and
“a” depend on the quality of rock mass, and they can be estimated by some empirical expressions
involving the Geological Strength Index (GSI). The GSI concept was introduced by Hoek et al. (1995),
and the value of GSI ranges from about 10 for extremely poor rock masses to 100 for intact rock. Further
details of this criterion can be found elsewhere (Hoek et al., 1995; Hoek and Brown, 1997).
In this method, geological information gathered from the Geographic Information System (GIS) and the
Mining Information System (MIS) are utilised to obtain geological cross-sections which are used in finite
element models for mesh building. Then, a number of two dimensional finite element modelling analyses
are carried out to determine land subsidence occurring due to mining operations. Finally, two dimensional
finite element results are interpolated to obtain three-dimensional surface topography after mining. Land
subsidence predicted from the modelling studies is compared with the GPS and/or Differential
Interferometry Synthetic Aperture Radar (DIn-SAR) measurements. If incompatibility of the results is
detected, finite element meshes should be optimised, and then reanalysed to obtain more compatible
results. Since the integration of the GIS and MIS data into the numerical modelling makes the numerical
solutions more accurate, this approach is called as “The Integrated Subsidence Prediction Technique–ISP-
Tech”.
Main steps of the approach can be summarised as follows;
- Division of the mining area into a number of parallel consecutive cross-sections by means of GIS
and MIS data,
- Data transfer from geological cross-sections into the finite element meshes to be analysed,
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- Performing two dimensional numerical analyses taking into account seam extraction orders to
obtain interstrata movements and surface deformations occurring due to mining activities,
- Correlation of 2D subsidence profiles gathered from a number of cross-sections to obtain 3D
surface topography after mining,
- Comparing numerical modelling test results to GPS and/or DIn-SAR measurements,
- If incompatibility of the results is observed, optimasition of the finite element models by
reconsidering rock mass properties and other important variables used in the analyses, and then
reanalyse the meshes to obtain compatible results,
- Application of the same procedure to neighbouring virgin coal areas to predict subsidence before
mining activities take place.
Apart from the numerical modelling studies, monitoring of surface subsidence is also important for this
approach. Several methods are currently used for this purpose (Ge et al., 2004; Deguchi et al., 2007;
Bauer, 2008; Akcin et al., 2012). These methods are useful for determining geometric and physical
changes caused by mine subsidence (Table 1) (Bawden et al., 2005). However, most of these techniques
have limitations, primarily because they measure subsidence on a point-by-point basis. Differential
Interferometry Synthetic Aperture Radar (DIn-SAR) is the most ideal technique which can measure the
ground movement (or deformation) of an entire area with an optimum resolution and spatial density
(Table 2) (Tesauro et al., 2000; Wang et al., 2004; Raucoules et al., 2007; Ng et al., 2010; Woo et al.,
2012). It is quicker, less labour intensive and hence less expensive compared to the conventional ground-
based surveying methods. Monitoring of subsidence propagation during and after mining operations gives
valuable information for undertaking remedial measures in time. The principle of interferometry is to
carefully exploit the engineered differences between the interferometric SAR (In-SAR) images
(Rodriguez and Martin, 1992). This method utilises three elements to form an interferometer: The “phase
coherent” part of the radar’s signals, the spatial separation of the satellite positions during its two passes
over the same area, and the information of the wavelength of the signals emitted from the radar system
(Fig. 4). The phase of the detected signals has a random part and a deterministic part. The random part is
“incoherent” while the deterministic part is “coherent.” If the random part of the phase in the reference
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image is different from that in the repeat image, the coherence of the phase differences in the
interferogram is lost. An imaging radar interferometer is capable of measuring the changes in the round-
trip distances of the electromagnetic signals between the satellite and the targets on the ground at the
reference time and the satellite’s next pass. Regarding the deformation monitoring, GPS is the most
powerful geodetic technique producing the most precise, reliable and exact results to detect pointwise
surface deformations. However, to keep wide areas under control, differential In-SAR is today’s most
useful geodetic technique. Because, GPS may need thousands of measuring points to monitor the area of
interest which can be controlled only through a pair of In-SAR images within a precise centimetre and
even millimetre range. Therefore, in the case studies, DIn-SAR was selected for measuring land
subsidence occurring due to mining operations. Measured values were then compared with the numerical
modelling predictions to prove the validity of the method proposed in this paper.
3. Case studies for the numeric application of the ISP-Tech
Today, prediction, monitoring and controlling of subsidence arising from coal mining activities are
essential for maintaining the stability of surface and underground structures. This is particularly important
where the mining activities take place under urban areas. Therefore, it is vital to evaluate the hazards
arising from subsidence occurrences in terms of the stability of structures and the influence of subsidence
effects on regional economy and social life.
As an example of the above mentioned circumtances, two different case studies are given as examples of
the proposed ISP-Tech approach (Fig. 5). In the first case, ISP-Tech was applied to Kozlu Mine in which
coal production has been made at depths between -300 m and -700 m below the sea level between 2007
and 2011. In the second case, subsidence predictions were made for a virgin hardcoal deposit which is
located beneath a highly populated area. Currently, a number of boreholes are drilled in this deposit to
obtain geological and geotechnical data for future mine planning studies. In this mine, almost ten different
coal seams would be worked at various depths.Therefore, decision of seam extraction orders and
dimensioning protection and control pillars should be effectively accomplished to minimise the adverse
effects of the subsidence on surface structures and to prevent inrush of water from the Black Sea.
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Case 1: Subsidence Evaluations for TTK, Kozlu Mine, Zonguldak-Turkey
In the first case, ISP-Tech method has been applied to a part of Kozlu Mine which operates perpendicular
to Blacksea along North-South directions, within the responsibility of Kozlu Coal Mine of Turkish
Hardcoal Enterprises (TTK) (Fig. 6). Coal productions in this region have been made from Westphalian-
A aged geologic formations (Fig. 7). Some of the seam specifications concerning worked coal panels
between the years 2007 and 2011 are given in Table 3 and Fig. 8.
In this mine, coal was produced at depths between 300 m and 700 m below the sea level. While the
estimated total coal reserve is about 77 million tonnes, extractable coal reserves is around 26.5 million
tonnes. In this mine, approximately 750 000 tonnes of hardcoal was produced annually. Although some of
the production panels were worked out with longwall mining with pneumatic backfilling between 1970
and 1980, mining method has become the advancing longwall mining with back caving since 1980. The
typical support system for the longwall coalfaces consist mainly of wood props and bars and wooden
chocks which are used in the coalface to provide breaking off line at the waste edge. Coal is extracted by
man power using picks or pick hammers.
For the analyses, mine production maps were transformed to 3-D vectorial data structure and integrated
into the Mining Information System (MIS) (Fig. 9). Similarly, geological vertical cross-sections at 200 m
intervals were obtained from MIS and utilised for generating the two-dimensional finite element meshes
to be analysed (Fig. 10 and Fig. 11). Geological cross-sections were transformed into full scale finite
element model meshes for stability analyses (Fig.12 and Fig. 13). Modelling studies yielded various
important data, including the vertical and total displacements, failed regions, sequential or cumulative
subsidence values for each calculation step, principal stress vectors and their distributions, normal and
shear stress etc. (Fig. 14, 15, 15, 17 and 18).
Results of the numerical modelling studies in which seam excavations were simulated by taking into
account excavation sequence of coal panels, were compared to ground deformation maps determined
from interpretation of PALSAR radar satellite data scanned between 2007 and 2011 by employing
Differential Radar Interferometry Technique (DIn-SAR) as well as GPS measurements made on site. The
deformation map obtained from PALSAR radar satellite over the area, GPS measurement points and
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geologic cross-section directions are illustrated in Fig. 19. On this map, vertical deformations (showed
with fringes) were determined from Interferometric Deformation Map and transferred to GIS
environment.
Overall results obtained from both DIn-SAR measurements and ISP-Tech predictions are given in Fig.
20a. According to the graphs, the coefficient of determination (R2) is calculated as 0.89 for linear
correlation between measured and predicted subsidence (Fig. 20b). However, the statistics was prompted
to compare objectively measured and estimated values. This comparison is called as conditional
unbiasedness. In this case, conditional unbiasedness was found as 0.83 (Fig. 20c). Moreover, t-test
evaluation was made on the measured and predicted subsidence data so that whether the means of two
groups statistically different from each other. Since the results comply with the requirements, i.e. T=
0.313, t20-0.95 = 2.09 and T<t, the differences between mean values are negligible and therefore mean
values are within acceptable limits.
Finally, surface topography changes because of the mining activities (i.e. subsidence bathtub) were
obtained by subtracting subsidence data from original ground surface, and the 3-D ground topography
after subsidence was drawn (Fig. 21 and Fig. 22).
Case 2: Subsidence Predictions for TTK Bağlık Coal Area-Zonguldak
Second study was carried out for virgin Bağlık coal deposit in which coal seams dipping downward
beneath Black Sea. Therefore, the risk of water inrush into mine workings must be evaluated during mine
planning stage as well as surface subsidence occurrence on the land. Numerical modelling studies can
also be utilised for this purpose. In this mine, almost ten different coal seams will be worked at various
depths and, therefore, order of seam extraction, widths of required protection and control pillars are
important and these pillars should be designed properly to prevent water inrush endangering mining
operations and also mitigating adverse effects of the subsidence on surface structures.
Fig. 23 shows the finite element model which is based on the geological cross-section taken 51000 S-N
direction in large-scale. Modelling studies were performed step by step basis (i.e. working only 1, 5 and
10 coal seams, respectively). Results of the work from which a wide array of outputs such as surface
subsidence, the extent of failed regions, principal stress distributions, safety factor contours etc. was
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depicted in Fig. 24 and Fig. 25. It should be noted that only one section is presented here as an example.
If more sections are worked out, 3-D outputs can also be obtained as in Case-1 given above. Since
propagation of natural or mining induced vertical cracks during mining operations is more crucial than
subsidence formation in undersea mining operations, a special emphasis must be given on this matter.
4. Disscussion of the Modelling Studies
Two different cases were presented to demonstrate capabilities of the ISP-Tech. In the first case, the aim
was to show the accuracy of the proposed approach. Therefore, results of the numerical modelling studies
were compared with site measurements obtained by the use of Din-SAR technique. Since the results were
found satisfactory, ISP-Tech can reliably be applied to neighbouring production areas for predicting
subsidence before starting mine operations (Fig. 20, Fig. 21 and Fig. 22). Subsidence information
obtained from these studies can be utilised for excavation layout and sequencing options and/or taking
preventative measures to mitigate subsidence damages. In the second case, subsidence predictions were
made for a virgin coal mine. In this mine, ten different coal seams are located under the residental area
and downward dipping beneath Black Sea. The results of the numerical modelling studies have shown
that increasing the number coal production panels results in developments of failed regions in overburden
strata (Fig. 24). In this case, the risk of water inrush from the sea should be considered as first priority.
Therefore, before starting mining operations, mine planning should be carefully realised by taking into
account seam excavation orders (i.e. harmonic mining) and careful planning and dimensioning of
protective pillars which are left between panels to maintain stability of entire mine structure.
5. Conclusions
In this study, a new subsidence prediction approach using two dimensional finite element modelling
technique together with Mining Information System (MIS), Geographical Information System (GIS) and
Differential Interferometry Synthetic Aperture Radar (DIn-SAR) is proposed for the prediction of surface
subsidence over underground mine areas. Results of the ISP-Tech showed that numerical modelling is a
useful tool for the prediction of ground subsidence, if geological and geotechnical rock mass parameters
are properly determined. Here, it should be noted that increasing the number of cross-sections used in the
analyses positively affects the accuracy of the results.
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The Integrated Subsidence Prediction Technique (ISP-Tech) resembles Magnetic Resonance (MR)
technique used in medical investigations in such a way that both of the methods cut the total into two-
dimensional slices (i.e. cross-sections) for examination. Then, subsidence information obtained from
these slices are evaluated and utilised for effective mine planning studies (i.e. seam excavation sequence
and optimum pillar requirements to minimise surface deformations, predicting surface subsidence prior to
panel excavations etc.). DIn-SAR measurements and ISP-Tech predictions realised for the first case study
showed that there was a good correlation between the predicted and the measured values. The coefficient
of determination (R2) is calculated as 0.89 for the linear correlation between the measured and the
predicted values. Since the results were found satisfactory, it was concluded that the application of the
method to the neighbouring virgin coal areas can be beneficial to predict surface subsidence before
mining activities take place. The second case study indicated that the number of simultaneously working
coal panels at different depths and seam extraction orders affect the final surface subsidence profile.
Results of the second case also indicates that special emphasis should be given to undersea mining
operations, since the risk of water flooding is more important than subsidence effects that may be
encountered on surface structures in underground mine regions. Finally, the results of the both case
studies indicated that the suggested approach (ISP-Tech) is a powerful and a versatile evaluation
technique for investigating complicated subsidence problems.
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Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 18
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Figure 19
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Figure 20
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Figure 23
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FIGURE CAPTIONS
Fig.1. Through type subsidence and influence area of subsidence on the longwall panel.
Fig.2. Data Collection steps for ISP-Tech.
Fig.3. Basic input data layers (in left) and some output (in right) of MIS that will be used in ISP-Tech.
Fig.4. Flow of InSAR processing (Akcin et al., 2010).
Fig.5. Study areas in the Zonguldak Kozlu region of Turkey.
Fig.6. Location of production panels and geologic cross-sections.
Fig.7. Production panels and boreholes showing geological ages of the formations.
Fig.8. 3D Longwall panels worked in the region (2007-2011) from MIS.
Fig.9. 3-D digital models in MIS of old graphical mine maps.
Fig.10. Sequential cross-sections used in modelling studies.
Fig.11. Finite element mesh derived from geologic cross section along S-N directions.
Fig.12 One of the cross-sections used for the numerical modelling studies.
Fig.13. Finite element mesh mounted on a geologic cross-section.
Fig.14. Vertical displacement contours along 46200 S-N direction.
Fig.15. Failed regions along S-N directions.
Fig.16. Cumulative temporal subsidence profiles along 46200 S-N direction.
Fig.17. Horizontal stress distributions on the surface along 46200 S-N direction.
Fig.18. Shear stresses on the surface along 46200 S-N direction.
Fig.19. Images of deformations map obtained with temporal DInSAR analyses; a and b from
RADARSAT images, c and d from PALSAR images (Deguchi et al., 2007).
Fig.20. Comparison of measured and estimated vertical surface subsidence.
Fig.21. Topographical changes after mine subsidence along 46200 S-N direction.
Fig.22. Original ground surface topography (a), and formation of subsidence after mining (b).
Fig.23. Finite element model which is based on the geological cross-section taken 51000 direction.
Fig.24. Strength factor contours and failed regions.
Fig.25.Total displacements contours and vectors (75 times exaggerated).
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Table 1. Land Subsidence measuring techniques (Bawden et al., 2005).
Method
Component
displacement (Dimension)
Resolution
(mm)
Spatial density
(samples/survey)
Spatial scale
(elements)
Sprit Level Vertical 0.1 - 1 10 - 100 Line-network
Total Station or EDM Horizontal 1 10 - 100 Line-network
Borehole
Extensometer
Vertical 0.01 – 0.1 1 - 3 Point
Tape Horizontal 0.3 1 - 10 Line - array
Invar wire Horizontal 0.0001 1 Line
Quartz tube Horizontal 0.00001 1 Line
GPS
Vertical
Horizontal
20
5
10 - 100 Network-line
InSAR
Range
Vertical (for PALSAR)
5 - 10
6 - 13
100000 -
10000000
Map pixel
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Table 2. Standards of geospatial data used in this study.
GEOSPATIAL
DATA
POSITIONAL ACCURACY
SCALE
ACCURACY
STANDARDS
(Horizontal (x or y)
limiting RMSE for various
map scales
at ground scale for
metric units)
Horizontal
(meter)
Vertical
(meter)
Range
(meter)
Arial Photogrammetric
DEM 0.15 1.00 1:1000
ANSSDA* Accuracy Stan.
0.25
X bant 30 m. SRTM
DEM
(For In-SAR analises)
Vertical accuracy
4.5m (for open area), 6.5m
(for forestry area) with
horizontal shifting
<1 mm than
effect to In-SAR
deformation
map
-- --
InSAR Deformation
map (from
RADARSAT)
-- --
6mm -- --
InSAR Deformation
map (from PALSAR) -- --
9mm -- --
Standard Topographic
Map 1.0 -- -- 1:5000
ANSSDA Accuracy Stan.
1.25
Mine Map (2.5D) 0.80 0.25 -- 1:1000 from Error Propagation
0.98
Mine Map (3D) 1.00 0.25 -- Digital
large Scale
from Error
Propagation+0.2
1.18
Orthofoto Map 0.15 1.00 -- 1:1000 ANSSDA Accuracy Stan.
0.30 *ANSSDA; American National Standard for Spatial Data Accuracy.
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Table 3. Specifications concerning coal seams worked (2007-2011).
PANEL
NO
SEAM
THICKNESS
PANEL
LENGTH
(L)
PANEL
WIDTH
(W)
PANEL
SLOPE
ANGLE (o)
AVERAGE
DEPTH
(H)
(m) (m) (m) Degree (m)
1 Acılık 2.18 268 132 30 528
2 Çay Batı 2.36 259 154 26
528
3 Çay III-IV 2.43 156 133 26
528
4 Milipero 2.09 104 152 21
460
5 Sulu 2.20 100 71 26
460
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Highlights
This study gives information regarding the importance of land subsidence in populated areas.
This study gives information about subsidence measurements using Differential Interferometry
Synthetic Aperture Radar (DIn-SAR).
We suggests a new subsidence prediction approach which uses both 2D-finite element modelling and
(DIn-SAR).
This study gives two case studies as examples for the application of proposed approach.
This work discusses the validity of the proposed subsidence prediction approach using predicted and
measured subsidence data.