IRON ORE MINERAL DEPOSITS EXPLORATION BY GROUND

MAGNETICS IN KINDANI AREA, MERU COUNTY, KENYA

BENSON MWIRIGI CYPRIAN [B.ED (Sc)]

I56/CE/24507/2012

A thesis submitted in partial fulfillment of the requirement for the award of the degree

of Master of Science in the School of Pure and Applied Sciences of Kenyatta University

November, 2016

iii

DECLARATION

This thesis is my original work and has not been presented for a degree in any other

University or any other award

Signature ………………………………… Date …………………………

Benson Mwirigi Cyprian

Department of Physics

Kenyatta University

We confirm that the work reported in this thesis was carried out by the candidate under

our supervision and approval as university supervisors

Signature …………………………………… Date …………………………

Dr. Willis Ambusso

Physics Department

Kenyatta University

Signature …………………………………… Date …………………………

Dr. John Githiri

Physics Department

Jomo Kenyatta University of Science and Technology

iv

DEDICATION

This thesis is dedicated to my mum Charity Karimi Ciauru and my dad Cyprian Ciauru

Anampiu who have made immense sacrifices to ensure I had all I needed to pursue

education to the highest possible echelons. I am eternally grateful.

v

ACKNOWLEDGEMENTS

I am grateful to God Almighty for the divine grace and enablement to pursue and

complete this work.

I sincerely appreciate my University supervisors Dr. Willis Ambusso (KU) and Dr.

John Githiri (JKUAT). You have patiently but firmly molded me in my academic

journey in the few months I have been under your supervision. I am grateful.

I am thankful to my friend Mr. Bonie Gitonga who helped me to get to the study area

and gave me important contacts. I am also grateful to my friend Mr. Nyamu Raria who

selflessly guided me in the Kindani area and rode me around each day on his bike

during my field survey. I also thank Mr. Kenneth Munene, for hosting me countless

times in his residence, and for encouraging me during my many trips to the University.

I also acknowledge my co-researcher Margaret Kebwaro with whom I started the

research at Kindani but who could not complete with me because her gravimeter broke

down. I know one day you will complete your work. I appreciate your encouragement

in the work. Special thanks to the Department of Infrastructure at Industrial area for

helping me do the chemical analysis.

May God bless you all, together with the many who I haven’t mentioned here who have

contributed to the success of this work.

vi

TABLE OF CONTENTS

Declaration…………………………………………………………………….……….. ii

Dedication……………………………………………………………………….…..… iii

Acknowledgements…………………………………………………………….…….... iv

Table of Contents……………..……………………………………………..…………. v

List of figures …………………..………………………………………………..….… vii

List of tables ……………………………………………………………………...…… ix

List of abbreviations………………………………………………...…………..………x

Abstract……………………..………………………………………………….…….… xi

CHAPTER 1: INTRODUCTION ................................................................................. 1

1.1 Background information ......................................................................................... 1

1.2 Geological setting ................................................................................................... 3

1.3 Statement of the research problem .......................................................................... 5

1.4 Objectives of the research project ........................................................................... 5

1.4.1 General Objective ................................................................................................ 5

1.4.2 Specific objectives ............................................................................................... 5

1.5 Rationale of the study ............................................................................................. 6

CHAPTER 2: LITERATURE REVIEW ...................................................................... 7

2.1 Introduction ............................................................................................................ 7

2.2 Iron ore forming processes ...................................................................................... 8

2.3 Ground magnetics survey theory ............................................................................. 9

2.4 The geomagnetic field .......................................................................................... 10

2.5 Elements of the geomagnetic field ........................................................................ 11

CHAPTER 3: MATERIALS AND METHODS ......................................................... 14

3.1 Introduction .......................................................................................................... 14

3.2 Survey equipment ..................................................................................................14

3.2.1 The Global Positioning System (GPS) ................................................................14

3.2.2 Flux-gate magnetometer .....................................................................................15

3.3 Methodology .........................................................................................................18

3.3.1 Data acquisation .................................................................................................18

3.3.2 Data processing ..................................................................................................19

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3.3.3 Data analysis...................................................................................................... 21

3.3.4 Reduction to the pole ........................................................................................ 21

3.3.5 Euler deconvolution .......................................................................................... 22

3.3.6 Forward modeling ............................................................................................. 23

3.3.7 Chemical analysis by energy dispersive spectroscopy (EDS) ............................. 24

CHAPTER 4 RESULTS AND DISCUSSIONS ..........................................................26

4.1 Introduction ...........................................................................................................26

4.2 Elevation of the Kindani Study area ...................................................................... 27

4.3Qualitative analysis of magnetic data ......................................................................28

4.4 Quantitative analysis ..............................................................................................31

4.4.1 Removal of regional trend ...................................................................................32

4.4.2 Euler deconvolution solutions and discussions ................................................... 35

4.4.3 Forward modeling results ................................................................................... 40

4.5 Chemical analysis results ...................................................................................... 45

4.5.1 Comparison of Kindani with Kimachia values ................................................... 48

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ................................ 50

5.1 Conclusions .......................................................................................................... 50

5.2 Recommendations ................................................................................................ 51

REFERENCES ........................................................................................................... 53

APPENDIX I: RAW DATA & DIURNAL CORRECTIONS TABLE ........................ 56

APPENDIX II: GEOMAGNETIC CORRECTIONS ...................................................59

APPENDIX III: BASE STATIONS (BS) DATA AND DIURNAL CURVES ............ 62

APPENDIX IV: PROFILE DATA ............................................................................. 66

APPENDIX V: ELEVATION DATA......................................................................... 73

viii

LIST OF FIGURES

Figure 1.1 Location of the study area ………...………………………………….…… 2

Figure 1.2 Geological map of the study area………………………………….……….4

Figure 2.1 Vector diagram on relationship between magnetization components……10

Figure 2.2 Elements of the geomagnetic field…………………………………...……12

Figure 3.1 Structural diagram of a flux-gate magnetometer…………………………..15

Figure 3.2 Working principle of a flux-gate magnetometer………………….……… 17

Figure 3.3 A diurnal curve graph…………………………….………….……….…… 20

Figure 3.4 Working principle of EDS……………………….……….……….………. 25

Figure 3.5 Example of a EDS spectrum ……………….……………………..……… 25

Figure 4.1 Distribution of the magnetic stations…………………………….……..… 26

Figure 4.2 Contour map of elevation of study area…………………………;………. 27

Figure 4.3 A 3-D topographic map of the study area……………….……..………… 28

Figure 4.4 A residual anomaly map of the Kindani area ………………….………….29

Figure 4.5 A 3-D Magnetic intensity surface map of the study area….…..………… 31

Figure 4.6 Profile cross-sections……………………………………….…..….……… 32

Figure 4.7 (a) Profile AA’ magnetic intensity trend……………….…………………. 33

Figure 4.7 (b) Profile BB’ magnetic intensity trend……………….………….……… 34

Figure 4.7 (c) Profile CC’ magnetic intensity trend………………….……….……… 34

Figure 4.7 (d) Profile DD’ magnetic intensity trend ……………….………….…….. 35

Figure 4.8 (a) Euler solutions along profile AA’ ………………………..…………… 36

Figure 4.8 (b) Euler solutions along profile BB’ ………………….……….………… 37

Figure 4.8 (c) Euler solutions along profile CC’ …………….……………….……… 38

Figure 4.8 (d) Euler solutions along profile DD’ …………………………….……… 39

Figure 4.9 (a) 2-D Modeling results along profile AA’ ……………………..………. 40

Figure 4.9 (b) 2-D Modeling results along profile BB’ ……………….……………. 41

Figure 4.9 (c) 2-D Modeling results along profile CC’ ……………...……………... 42

ix

Figure 4.9 (d) 2-D Modeling results along profile DD’ ……………………….……. 43

Figure 4.10 Rock samples from the Kindani study area …….………………..….… 45

Figure 4.11 Areas where the rock samples were collected …………………….…... 46

x

LIST OF TABLES

Table 3.1 Structural indices for different geological structures……………………. 23

Table 4.1 The I.G.R.F values for Kindani area ………………………..….……….. 36

Table 4.2 Summary of the 2-D modeling results ……………………….….….…… 44

Table 4.3 Chemical analysis results of Kindani area samples ………...….….……. 47

Table 4.4 Chemical analysis results of selected samples from Kimachia area.....… 49

xi

LIST OF ABBREVIATIONS

GDP - Gross Domestic Product

IGRF - International Geomagnetic Reference Field

BIF - Banded Iron Formations

nT - nano Tesla

2-D - Two dimension

3-D - 3 Dimension

GPS - Global Positioning System

RTP - Reduction To Pole

EDS/EDX - Energy Dispersive Spectroscopy

RMI - Residual Magnetic Intensity

SI -International System of Units

xii

ABSTRACT

Recent geophysical surveys have reported presence of iron ore deposits within Meru

County. It has been speculated that there could be more deposits within the region.

Ground magnetic surveying was used to detect magnetic rocks within host formations

in Kindani area of Maua. A fluxgate magnetometer was used to measure the vertical

component of the Earth’s magnetic field in some 98 stations, covering an area of about

25 km2. Diurnal and geomagnetic corrections were then done on the data. A contour

map that delineates anomalies in the study area was generated using Surfer 10 software.

The map shows varied anomalies spread out within the region. The anomalies mostly

trend on NW-SE and SW-NE directions. Four cross sectional profiles were drawn

across various anomalies and the digitized data used to draw 2-D line graphs. The data

obtained was used in 2D modeling using Euler software which gives estimated depths

to magnetic structures at between 0m-1500m. Mag2dc modeling gives bodies of

susceptibility between -1.724 SI to 1.7624 SI. The depth to top of magnetic structures

ranges from 0 m to 136 m, which indicates shallow structures. A chemical analysis of

some rock samples indicates quantity of Fe2O3 at an average of 25%. From the study,

there is confirmation of iron ore deposits in the region, which confirms presence of

extended iron deposits within Meru County. There is need to survey the entire Kindani

plains, using different geophysical methods to delineate more deposits for possible

exploitation

1

CHAPTER 1: INTRODUCTION

1.1 Background information

An initial reconnaissance geological survey by Rix (1967) acknowledges presence of a few

minerals but notes however, that the minerals were all present in very small quantities and

were unlikely to prove of any economic value. An initial geological reconnaissance survey of

the Meru region by Mason (1955) indicates that no mineral deposits of importance were

found in the area and therefore no intensive prospecting was recommended.

However, recent surveys by Abuga (2013) and Kassim (2014) confirm presence of

significant deposits of iron ore in the Kimachia area in Meru County. Chemical analysis

results revealed over 90% iron content in some rock samples. They speculated that these

deposits could be part of more deposits within the region. Rock samples collected in the

Kindani area show greater potential for iron due to their high magnetic properties. This

therefore necessitates more research. This study sought to confirm presence of more iron

deposits in Kindani area which could confirm presence of an iron rich belt as suggested by

Abuga et al. (2013).

The intended survey was undertaken by Ground magnetics. Magnetic surveys are used to

investigate subsurface geology on the basis of anomalies in the Earth’s magnetic field (Keary

et al., 1984). The anomalies are caused by local bodies which cause magnetic highs and lows

compared to the values of the earth’s geomagnetic field predicted at a particular place by the

International Geomagnetic Reference Field (IGRF).

The Kindani area is about 7 kilometers from Maua town and North East of Meru town. The

survey area covered the small markets of Kilili, Ndila, Junction and Kindani. The surveyed

area is shown in figure 1.1

2

Fig 1.1: Location of Kindani study area. Simplified from Kenya Government maps (1983).

Map of Irereni, Sheet 109/3.

3

1.2 Geological setting

The area under study is bound by the longitudes 38000’ E and 38

005’ E and latitudes 0

005’

and 0015’. Kindani area lies within the outskirts of Nyambene ranges. The Nyambene

volcanic range is stretches in a north-east to south west direction from the foothills of Mt.

Kenya and rises to an elevation of 7000 feet. Rocks in the Nyambene volcanic series are

young Tertiary, Pleistocene and recent extrusive rocks and subordinate sediments. The

Pleistocene-recent lava is mostly olivine basalts. The basement system metamorphic rocks

comprise gneisses, plagioclase amphibolites, crystalline limestone and quartizes (Rix 1967).

A geological map of the area is shown in figure 1.2

Earlier tests for valuable minerals were negative, but magnetite was found to be present in

some concentrates examined (Rix, 1967) albeit in small quantities. In the recent times

however, studies in the adjacent areas by Kenyatta university students reveals that the area

could have considerable amounts of iron ore (Abuga et al., 2013; Kassim, 2014). Samples

collected in Kindani area were found were found to be highly magnetic and with a high

specific density which indicates a high prospect for iron ore.

4

Figure 1.2: Geological map showing study area and adjacent regions (Rix, 1967)

5

1.3 Statement of the research problem

Recent geophysical studies have revealed presence of iron ore deposits in the Kimachia

region of Meru County. There has been need therefore, to establish if the deposits cover a

wider region. Preliminary rock samples collected from Kindani indicates that the area indeed

has potential for iron ore deposits. This study was therefore undertaken to investigate for

more deposits in the area. In addition, Kenya has an increasing demand for iron for industrial,

construction and infrastructural development. A lot of foreign exchange is lost every year in

importing large amounts of iron and iron products into the country. To facilitate the much

needed development in the country, local and cost effective iron will be needed. The results

from the study will provide a basis for making recommendations for possible exploration for

iron ore in the area.

1.4 Objectives of the research project

1.4.1 General Objective

The main objective of this study is to carry out a ground magnetic survey in the Kindani area

to locate areas of possible iron ore deposits and the extent of coverage in the region.

1.4.2 Specific objectives

To carry out magnetic measurements and reduce the magnetic data of the Kindani study area

To image depths to any magnetic sources using Euler deconvolution technique

To generate forward models of the magnetic data that quantify size and extent of any

detected magnetic bodies that may be iron ore

To determine mineral compositions of rock samples from the study area using chemical

analysis

6

1.5 Rationale of the study

Ground magnetic surveys are regularly used for the direct detection of mineralizations such

as skarns, massive sulphides, iron ore deposits, kimberlites and others. Ground magnetics is

frequently preferred because relative to other geophysical methods, acquisition of magnetic

data is rapid and cost effective, especially when surveying small areas such as Kindani. Due

to recent enhancements in instrumentation and data filtering, processing and display

methods, magnetic data is rapidly processed and interpreted. Maps of magnetic anomalies

often reveal magnetic signatures that often reveal regions of mineralization. This research

therefore provided information about iron mineralization in Kindani and also provided

information useful in updating the geology of the area.

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CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

The magnetic method is one of the oldest methods of geophysical exploration (Gunn and

Dentith, 1997). Esperson (1997) stated that magnetic surveys were used in Sweden since mid

17th Century. Recent improvements in instrumentation and navigation make it possible to

map crustal sections at various scales. In addition, magnetic method is usually the primary

tool in search for minerals. It is used for mapping basement structures, defining lithologic

contacts, locating intrasedimentary faults among other uses. The method has increasingly

been used in different realms of exploration such as search for minerals, oil and gas,

geothermal resources, ground water and natural hazards assessment (Nabighian et al., 2005).

The magnetic method mainly utilizes the difference in magnetic susceptibilities of minerals

and the host rocks. In iron exploration, for example, the magnetic signature of the iron

formation is usually one or two orders of magnitude greater than that of the rock where the

deposit is found.

Magnetic surveying has been used in the Albuquerque basin to map aquifers. Magnetic maps

in the area showed intrabasin faults and buried igneous rocks (Grauch et al., 2001).

In Kenya, magnetic surveying has widely been used as a reconnaissance survey method in

geothermal exploration. Adero et al. (2014) used the magnetic method to determine the

subsurface structure of the Homa-Hills geothermal prospect area. They successfully used the

method to delineate geothermal heat sources in the study area. Many other studies have been

carried out in different geothermal prospects in the country and especially within the Rift

valley.

8

In Northwestern Tasmania, magnetic surveying played a key role in delineating the massive

Savage River magnetite deposit (Eadie, 1970). The deposit is thought to have a magmatic

origin (Coleman, 1975). It is associated with a magnetic anomaly of more than 10, 000 nT.

Kerr et al., (1994) describes the magnetic signatures of BIF hosted iron ore deposits from the

Pilbara region. Structural and stratigraphic control of the mineralization is important here and

the mapping of the appropriate stratigraphic horizons and identification of structures such as

folds and faults are important in interpretation of magnetic data (Gunn and Dentith, 1997).

The ore appears as zones of reduced magnetic intensity within the magnetic BIF zones.

Kassim (2014) successfully used the ground magnetics geophysical method to delineate

regions of small scale iron ore deposits in Kimachia area. The study, carried out on the

eastern parts of Nyambene ranges confirms of small scale iron deposits. The deposits are

suspected to be part of a larger iron rich zone (Abuga et al., 2013). Rix (1967), from the

geological survey of the Kinna area confirms presence of small quantities of garnets and

magnetite in the region. These occurred at MelkaLorni and Kalusikaumu. He however noted

that they were unlikely to prove of value. The work was however for general mapping

purposes and therefore no extensive geophysical studies were carried out.

2.2 Iron ore forming processes

Iron ore, is formed in the continental crust by a variety of ways. This may involve

sedimentation, igneous activity or by physical weathering. Bedded sedimentary iron ore

deposits are thought to occur as a result of mineral precipitation from solutions present

during the Precambrian period (2.6 to 1.8 billion years ago). Sedimentation may lead to

formation of Banded Iron Formations (BIF) or they may occur as ironstones. Iron content in

BIFs may range from 25% to 40%. Ironstones are formed due to intense weathering of

continental crust. They contain about 20-40% iron (Bett and Maranga, 2012).

9

They may occur as pellets of limonite, hematite or chamosite. Iron ore formation by igneous

activity is mostly by magmatic segregation. The deposits may occur as magnetite, hematite

or ilmenite. When Iron bearing minerals decay physically or chemically, they may lead to

concentrations of iron oxides (Bett and Maranga, 2012).

In Kenya, Iron ore occurs majorly in Taita, Kitui, Tharaka-Nithi and Siaya-Samia Hills. Bett

and Maranga (2012) classify the ores found in Kenya into 4 classes: the magmatic,

sedimentary, detrital and residual. The magmatic occur within the basement system rocks and

occur majorly in Western, Eastern and North-Eastern parts of the country. The sedimentary

occur as Archeans chists in Voi and as Precambrian banded iron stones in Nyanza. The

detrital occur as black sands at the coast and the residuals occur as lateric iron stones mainly

in western Kenya.

2.3 Ground magnetics survey theory

The aim of magnetic surveying in general is to investigate subsurface geology on the basis of

anomalies caused in the earth’s magnetic field (Keary et al., 1984). It’s used for detailed

mapping in order to understand the subsurface geology of an area (Kayode et al., 2010).

Exploration for iron based on its magnetic effect represents the earliest use of geophysics in

mineral exploration (Gunn and Dentith, 1997). It’s used to locate places with unusual

magnetizations mainly due to locally buried bodies. Rocks usually contain magnetic minerals

of different quantities and different magnetic susceptibilities. A material placed in a

magnetic field may acquire magnetic properties which it may later lose (induced magnetism)

or it may acquire magnetism that then becomes permanent (remanent magnetism). It is this

induced or remanent magnetism that eventually causes anomalies in the measured magnetic

field at a location. The extent of the magnetization depends on the magnetic susceptibility of

the rock. The induced magnetization Ji intensity is proportional to the magnetizing force (H).

10

kHJ i

(2.1)

Where k is the magnetic susceptibility of the material.

A rock containing magnetic minerals may have both induced (Ji) and remanent (Jr)

magnetism. The resultant magnetic vector (J) has a magnitude that affects the amplitude of

the magnetic anomaly and its orientation affects the shape of the anomaly. A vector diagram

of the 3 vectors is as shown below.

Ji

Jr J

Fig 2.1 Vector diagram on the relationship between Induced (Ji) remanent (Jr) and total (J)

magnetization components (Keary et al., 1984)

Ground magnetics are usually carried out over small areas. In carrying out a ground magnetic

survey, the 3 components of the magnetic field may be measured. These are the vertical,

horizontal or the total component. The vertical component and the total components are the

most widely used in past studies to delineate faults, depth to magnetic basements and other

geological structures (Folami, 1992).

2.4 The geomagnetic field

The earth’s main magnetic field is believed to be produced by the fluid outer core of the

earth. The major constituent of the fluid core is believed to be chiefly iron, with small

percentages of nickel and some non-metallic light elements such as silica, sulfur or oxygen.

The liquid cools on the outside, becomes denser and therefore sinks towards the inside of the

outer core. It is then replaced by warmer less dense fluid. As a result of these movements,

11

convection currents are produced by the liquid metallic matter which moves through a weak

cosmic magnetic field and which subsequently generates induction currents (Nettleton,

1976). It is these induction currents that then generate the earth’s magnetic field (Telford et

al., 1976). The rest of the geomagnetic field is caused by electric currents in the ionized

layers of the upper atmosphere. These appear in the form of diurnal variations, lunar

variations and magnetic storms. Diurnal changes are the changes on the field on a daily basis

with amplitudes of between 20 – 80 nT (Keary et al., 1984). These changes necessitate

diurnal corrections of magnetic data when carrying out magnetic surveys. Magnetic storms

may cause disturbances of up to 1000 nT. Magnetic storms occur when charged solar

particles enter the earth’s ionosphere. If a magnetic storm is detected, in the process of a

magnetic survey, the survey has to be discontinued because the values obtained in such a

study would be grossly misleading. The total geomagnetic field ranges from 25000 nT in

magnitude to about 65000 nT. The intensity of the geomagnetic field decreases from the

poles towards the equator. Changes in the geomagnetic field occur continually, both over

short periods as well as over long periods. The changes in variation of a year or more are due

to changes in the conductive core (secular variation).

2.5 Elements of the geomagnetic field

The earth’s magnetic field is a vector. The magnetic elements are used to describe the field

which has different components. If a magnet is freely suspended, its north pole points to the

magnetic north of the earth. The magnetic north lies at an angle to the geographic north of the

earth. The geomagnetic elements are as illustrated in figure 2.2 below.

12

Fig 2.2 Elements of the geomagnetic field (After Lowrie, 1997)

Where:

Z is the vertical component of the magnetic field.

H is the horizontal component which is the magnetic meridian (magnetic north).

D is the angle of declination (angle between the magnetic meridian and the geographic

meridian).

I is the angle of inclination (angle by which the total magnetic vector F dips from the

horizontal).

F is the total magnetic field (sum of the horizontal and vertical components of the

geomagnetic field).

X is the direction of the geographic north and

Y is the East direction

13

These geomagnetic elements are related to each other by these equations:

DIFX coscos (2.2)

DIFY sincos (2.3)

IFZ sin (2.4)

2222 ZYXF (2.5)

X

YD arctan (2.6)

22arctan

YX

ZI (2.7)

14

CHAPTER 3: MATERIALS AND METHODS

3.1 Introduction

In this study a Fluxgate magnetometer was used to measure the vertical component of the

magnetic field in each station. Its working principle is discussed in this chapter. Data

collection, reduction and the process of analysis is also discussed. Energy Dispersive

Spectroscopy is the method used for chemical analysis of the rock samples. A brief

description of the process has also been discussed.

3.2 Survey equipment

3.2.1 The Global Positioning System (GPS)

This is a satellite based navigation system made of 24 satellites that orbit the earth. The

system was invented by the US Department of Defense for military use but was later allowed

for civilian use. The satellites circle the earth about every 12 hours in a precise orbit and

continuously transmit signal information to the earth. They orbit the earth at about 12,000

miles, travelling at about 7,000 miles every hour. Each satellite weighs about 2,000 pounds

with a span of about 17 feet.

GPS receivers on the earth receive signals from the satellites and then use trilateration

method to calculate the user’s exact position. To calculate the latitude and longitude (2-D)

position of a user, the GPS receiver must connect to at least 3 satellites. The satellite orbit the

earth in such a way that at least four of them can be located from any point on the surface of

the earth. To determine a 3 dimensional location (latitude, longitude and elevation) the

receiver must locate at least four satellites.

The advantage of using the GPS system for positioning is that it works well in different

weather conditions, in any part of the world. It is available for free as long as one has a GPS

15

receiver. The signal easily penetrates clouds, plastic and glass. However, it may not penetrate

thick solid objects such as mountains or concrete. The system therefore is commonly used for

field positioning as it may not work well inside buildings, in caves or in water.

Some mobile phones today are GPS enabled and they have applications that enable GPS

positions to be read. In this study, a handheld Garmin Etrex GPS machine was used.

3.2.2 Flux-gate magnetometer

The flux-gate magnetometer is made up of two parallel cores of high susceptibility, such that

they can be magnetized by the geomagnetic field.

Fig 3.1 Structural diagram of a flux-gate magnetometer (After Reynolds, 2011)

The primary coils are wound such that when a primary current is passed through them (Fig

3.2A) they get magnetized in opposite directions. A secondary coil, wound on the primary

coils detect the changes in the magnetic flux in the primary coils. As a result of the changing

flux, a voltage is then induced in the secondary coils. In the absence of an external field, the

coils will saturate every half cycle (Fig 3.2B). The voltages induced in the secondary have

S N

16

opposite polarities because the coils are wound in opposite directions. This yields zero net

voltage (Fig 3.2C). In the presence of a magnetic field however, the component of field

parallel to the cores causes one core to saturate before the other. The voltages now therefore

do not cancel out and we get a net voltage (Fig 3.2 D, E). The output voltage is calibrated in

terms of magnetic field since its proportional to the magnetic field strength. The flux-gate

magnetometer has an accuracy of about 1 nT (Keary et al., 1984; Lowrie, 1997)

17

Fig 3.2 Working principle of a flux-gate magnetometer (After Reynolds, 2011).

18

3.3 Methodology

3.3.1 Data acquisition

Magnetic surveying aims at locating rocks or minerals with anomalous magnetizations which

reveal themselves as anomalies in the intensity of the earth’s magnetic field (Abdelraham and

Kassa, 2005; Adagunodo et al., 2012). The ground magnetics survey that was carried out in

Kindani region aimed at locating possible sources of iron ore in the region. Magnetic

surveying involves 3 major steps:-

i) Measuring the terrestrial magnetic field at some predetermined points.

ii) Correcting the magnetic data for known changes.

iii) Comparing the resultant value of the field with the expected value at each measurement

station (Lowrie, 1997).

The expected of the field is the value given by the International Geomagnetic Reference

Field (I.G.R.F) the difference between the observed values and the I.G.R.F values gave the

magnetic anomaly which was then appropriately processed and interpreted.

A fluxgate magnetometer was used to measure the vertical magnetic intensity at each station.

A total of 98 magnetic stations were established in the area. A base station was also

established and severally occupied during the day so that its values would be used for diurnal

corrections. A total of 4 readings were taken in each station and the average obtained. This

practice enhances accuracy of the data (Maunde et al., 2013). In addition other readings that

were taken include elevation at each station, time of taking magnetic readings and the

position (Easting & Northing) of each of the stations.

19

During data acquisition, all magnetic objects including mobile phones, metallic belts were

kept away. All stations were situated at a safe distance of about 50m from concrete buildings

and power lines to enhance integrity of the magnetic data.

For a small target area such as Kindani, ground magnetics was preferred because it gives a

detailed pattern of the study area over the region since the measurements were taken close to

the anomaly. The survey stations were distributed along pre-determined positions in the

region. Exceptions were made where extreme terrains were encountered. Stations were

spaced about 500m apart. They were located at least 50m away from tarmac roads and from

buildings to reduce the chance of interference from unwanted magnetic fields.

3.3.2 Data processing

The first step in data processing was applying corrections to the collected magnetic data. The

following corrections are usually carried out on magnetic data:-

i) Diurnal corrections

This is the correction that is necessitated by the changes in the intensity of the geomagnetic

field in the course of the survey. These changes occur due to interferences from the

ionosphere. This correction was applied by taking readings every hour or two at the base

station within the survey area, and the drifts removed at the end of each day. If the readings

were found with very huge differences, they were interpreted to have been caused by

magnetic storms.

To make this correction a diurnal line graph (Fig 3.3) was drawn using magnetic data

collected at the base station. Readings were referenced to the first value taken at the base

station.

Corrected Value = Observed value ± diurnal corr.(∆d)

20

Fig 3.3 A diurnal curve graph

In the figure 3.3 above for example, between 9.30 am and about 12.00 noon the reading at

the base station dips. Readings taken at other stations at this time therefore must be lower

than the observed values. The diurnal correction is therefore subtracted from the observed

values. Readings taken between 12 noon and 1am should be higher than the observed values

and therefore the diurnal difference is added. The diurnal graph curves of the work

undertaken in Kindani area are shown in appendix III.

ii) Geomagnetic corrections

This is the correction which removes the effects of a reference geomagnetic field from the

survey data (Keary et al., 1984). This correction was done by subtracting values given by the

IGRF from the magnetic data obtained after doing diurnal corrections. The IGRF values for

the stations and the geomagnetic corrections done are shown in appendix II. These values

10220

10240

10260

10280

10300

10320

10340

10360

9 10 11 12 13 14 15 16 17

MA

GN

ETIC

INTE

NSI

TY

TIME (HRS)

Series1

∆ d

21

were obtained from public domain software mathematical models. The inputs are latitude,

longitude elevation and the date of the observation (Maunde et al., 2013).

iii) Terrain and elevation corrections

These are correction of altitude. However this correction was unnecessary because the

vertical gradient is 0.03nTm-1

at the poles and -0.015nTm-1

at the equator (Keary et al.,

1984). This difference is insignificant and therefore elevation and terrain correction were not

applied to this data.

3.3.3 Data analysis

There are several methods of presenting magnetic data (Kayode et al., 2010; Obot and

Wolfe, 1981). Presentation involved drawing magnetic contour maps and using traverses to

draw magnetic profiles. Depth estimates were imaged using Euler Deconvolution software

developed by Cooper (2004).

3.3.4 Reduction to the pole

This is a data processing technique that recalculates the total magnetic intensity data as if the

inducing field had a 900 inclination. The acquired anomaly is therefore one that would be

measured at the north magnetic pole, where induced magnetization and ambient field are

directed downwards (Blakely, 1995; Githiri et al., 2011). The major effect of this

transformation, and which makes interpretation much easier, is that dipolar anomalies are

transformed to mono polar anomalies. These single pole anomalies are usually centered over

the causative magnetic subsurface bodies.

Reduction to the pole is usually unreliable at low magnetic latitudes where northly striking

magnetic features have little magnetic expression. Some bodies have no detectable magnetic

anomaly at zero inclination (Blakely, 1995; Githiri et al., 2011). Another disadvantage of

22

using RTP to transform magnetic data collected very close to the magnetic equator is that a

large correction would need to be made for the amplitude of anomalies. Validity of reduction

to the pole is therefore advised only for declinations greater than 100.

In the Kindani survey area declination was -19.70 and therefore reduction to the pole was

considered a reliable method to transform the magnetic data. The software Euler was used in

reducing to the pole of the profile data. It was used to delineate areas of magnetic sources and

their approximate depths. The software also calculates and displays variations in the

horizontal and vertical gradients of the magnetic data along the profiles.

Euler deconvolution is advantageous in that it provides a fast method to image approximate

depths to anomalous magnetic bodies. The identified locations and depths to the causative

bodies are independent of magnetization directions or distortion of field caused by remanent

magnetization (Githiri et al., 2011). The kind of magnetic features can also be inferred from

the optimum structural index selected.

3.3.5 Euler deconvolution

Euler deconvolution is a technique which uses potential field derivatives to image subsurface

depth of a magnetic or gravity source (Hsu, 2002, Githiri et. al., 2011). Euler deconvolution

is expressed as:-

TBN

zTzz

yTyy

xT

000x-x (3.1)

Applying the Euler’s expression to profile or line oriented data (2D source), x-coordinate is a

measure of the distance along the profile and y-coordinate is set to zero along the entire

profile (Adero B et al., 2014). Equation 3.1 is then written as:-

TBN

zTzz

xT

00x-x (3.2)

23

where:

x0, z0 is the coordinate position of the top of the body

Z is the depth measured as position down

X is the horizontal distance

T is the value of residual field.

B is the value of the regional field

N is the structural index which is a measure of fall-off rate of the magnetic field. It depends

on the geometry of the source (El Dawi et al., 2004; Adero et al., 2014).

The structural indices for different possible geological structures (Reid et al. 2011) are as

follows.

Table 3.1 Structural indices for different geological structures

Structural Index Geological Structure

0 Contact

0.5 Thick step

1 Sill/Dyke

2 Vertical pipe

3 Sphere

3.3.6 Forward modeling

Forward modeling involves determination of parameters like geometry, depth, and density

contrast. This is a trial and error method that is used to obtain the best fit to the observed

anomalies. It involves constructing an initial model for the source body using prior

geological knowledge of the area. Adjustments are then made to the model until an

24

acceptable fit is obtained. This was done using mag2dc computer software program

developed by Cooper (2004). Description of the method of the program mag2dc can be found

in the work of Talwani and Heirtzler (1964).

3.3.7 Chemical analysis by energy dispersive spectroscopy (EDS)

Energy dispersive spectroscopy is an analytical technique that can be used to determine

elemental composition of a sample or for chemical characterization. In this technique, a beam

of high energy particles such as electrons or protons is directed to a sample. High energy x-

rays may also be used.

An atom is made of a dense nucleus which is surrounded by electrons orbiting in energy

levels (energy shells). The shells are usually labeled K, L, and M beginning with the

innermost shell. An atom will usually have unexcited electrons. When a beam of high energy

x-rays hit the electrons, it excites them. The electrons in the inner shells may get dislodged

from their shells as a result of the excitation. These then leave electron holes in these inner

shells. When this happens, the atom becomes unstable. To regain stability, electrons from the

outer shells must move to refill the inner shells. However, electrons in outer shells have

greater energy than those from the inner shells. An electron that moves to the inner shell

must therefore lose some energy. This energy is lost in form of x-rays. These x-rays emitted

from the sample atoms are characteristic in energy and wavelength both to the element of the

parent atom and also to the particular shell that released them. An EDS detector can therefore

be used to separate these characteristic x-rays of different elements into an energy spectrum.

An EDS computer software system is then used to analyze the energy spectrum in order to

determine the abundance of each specific element.

25

Fig 3.4 Working principle of Energy Dispersive Spectroscopy (Simplified from Heath, 2015)

An EDS spectrum is usually portrayed as a plot of x-ray counts vs. energy in KeV as shown

in figure 3.5. The energy peaks correspond to the various elements in the sample and can be

analyzed from the energy spectrum.

Fig 3.5 Example of EDS spectrum, (Goldstein et. al., 2003)

26

CHAPTER 4: RESULTS AND DISCUSSIONS

4.1 Introduction

A total of 98 magnetic stations (locations where magnetic readings were taken) covering an

area of about 25km2 were established. The surveyed area was bound by Northings 19000-

24000 and Eastings 389000- 394500. The distribution of the stations is as shown in figure

4.1 below.

Fig 4.1 Distribution of the magnetic stations

27

4.2 Elevation of the Kindani Study area

Figure 4.2 below is a contour map showing the topography of the area. The elevation data

collected from the field (shown in appendix V) was used to plot the contour map. The blue

and black colour shows the areas with the lowest elevations while the hotter colors of red and

yellow indicate the regions that rise the highest within the region. The area generally slopes

from the North West towards the South East. However, apart from the small hill at the North

Western part of the map, the area slopes gently towards the East. The highest point rises to

about 1140m while the lowest point slopes to about 760m above the sea level.

Fig 4.2 A contour map of the elevation of the study area

Meters

28

The topography of an area is important because sometimes minerals occur in hilly areas of a

place, or at the low lands. The elevation map would therefore help to indicate whether there

is a relationship between the Kindani terrain and the deposits. A 3-D topographical map of

the area is shown in figure 4.3 below.

Fig 4.3 3-D topographical map of the study area

4.3 Qualitative interpretation of magnetic data from Kindani area

The residual magnetic intensity data obtained after doing diurnal and geomagnetic

corrections was used to draw a contour map (Fig 4.4). The data used is shown in appendix II.

29

Fig 4.4 A residual anomaly map of the Kindani area

Qualitative interpretation of a magnetic map begins with a visual inspection of the shape,

trend of the major anomalies, and examination of the characteristic features of each

individual anomaly. Such features may include the relative locations and amplitudes of the

positive and negative parts of the anomaly and the aerial extent of the contours and sharpness

of the anomaly, as distinguished by the spacing of the contours (Nettleton 1976; Selim and

Aboud, 2013).

nT

30

Figure 4.4 above shows a color range of magnetic residual anomaly values, with red as the

highest and blue-purple being the colors with the least values. The highest anomaly rises to

about 1600 nT while the lowest values are at about -3000nT.

The anomalies in the region show 3 major orientations: SE-NW, SW-NE and the E-W

orientations. The longest positive anomaly, marked A is elongated on a SE-NW direction. It

has values that rise to about 1600 nT on the lower end and to about 200 nT on the upper end.

The high amplitudes suggest near surface magnetized bodies. Such high values are usually

characteristic of highly magnetized ores such as those containing high magnetite content.

Anomaly B is a magnetic high circular anomaly appearing at the center of the study area. It

also rises to a high of about 1600 nT.

Anomalies marked E and F are negative anomalies with anomaly F getting to a low of up to -

3000nT. The anomalies marked E have a SW-NE orientation. The anomaly F has a SE-NW

orientation which is a common trend with the other major anomaly in the neighborhood,

anomaly A. Since these negative anomalies occur near the positive anomalies, they could be

the negative poles on the same bodies since magnetism is a dipolar quantity.

The anomalies D, G and H represent more subdued highs. These lower amplitude anomalies

suggest deeper buried bodies which may be of volcanic origin. Area C and the lower parts of

H are more magnetically quiet areas and suggest absence of highly magnetized bodies. These

areas seem to have homogenous non-magnetic material.

Figure 4.5 is a 3-D map of the magnetic intensity variations of the study area.

31

Fig 4.5 A 3-D Magnetic intensity surface map of the study area.

4.4 Quantitative analysis

Quantitative analysis in this study involved interpretation of profile data and forward

modeling. Four profiles AA’, BB’, CC’ and DD’ were chosen, cutting across major

anomalies observed in the study area. Each profile was chosen to cut through both magnetic

highs and adjacent lows because magnetism is a dipolar quantity. Anomalous objects are

therefore expected to show both positive and negative poles in observed data. The profiles

are illustrated in figure 4.6 below.

EASTINGS NORTHINGS

nT

32

Fig 4.6 Profile cross-sections

4.4.1 Removal of regional trend

Before profile data can be used in further analysis, it is important to remove the regional

trend from the data. Magnetic anomaly fields are often characterized by a smoothly varying

field due to magnetic response of large scale background structures such as basement rocks.

The local anomalies are superimposed on this regional field. In this study, the interest was

the near surface anomalies that have short wavelength and high frequency. It is therefore

necessary to remove the longer wavelength, low frequency anomaly. Since the data being

considered here is 2-dimensional profile data, trend removal was reliably done graphically.

nT

33

Linear trend analysis was done and the regional field subtracted from the corrected magnetic

data using the equations below for profiles AA’, BB’, CC’ and DD’ respectively.

472.85 -0.089x - = Y (4.1)

695.99 -0.0022x = Y (4.2)

1105.09 -0.009x =Y (4.3)

1507.85 -0.134x = Y (4.4)

The profile trends are illustrated in figure 4.7 (a) – 4.7 (d). The red graphs represent the

graphs with regional trends while the blue graphs show the graphs without the regional

trends. The profile data is shown in appendix IV.

Fig 4.7 (a) Profile AA’ magnetic intensity trend

-2000

-1500

-1000

-500

0

500

1000

1500

2000

-1000 0 1000 2000 3000 4000 5000 6000

Mag

net

ic In

ten

sity

(nT)

Distance (m)

Without trend

With trend

34

Fig 4.7 (b) Profile BB’ magnetic intensity trend

Fig 4.7[c] Profile CC’ magnetic intensity trend

-2500

-2000

-1500

-1000

-500

0

500

1000

1500

2000

-1000 0 1000 2000 3000 4000 5000 6000

nT

Distance (m)

Without trend

With trend

-3000

-2000

-1000

0

1000

2000

3000

0 2000 4000 6000 8000

nT

Distance (m)

Without trend

With trend

35

Fig 4.7 (d) Profile DD’ magnetic intensity trend

4.4.2 Euler deconvolution solutions and discussions

Euler 1.0 software was used to map the depth to subsurface magnetic structures in the survey

area. A structural index of 1 was adapted for this work as being the one best representing the

structural formations of the area. Estimating the depth to the anomaly also involved reduction

to the pole, calculation of the vertical and horizontal gradients of magnetic field data,

choosing window sizes and the structural index (Adero et al., 2014). A window size of 11

was chosen, with a X-separation of 255.71 m and Y separation of 127.86m.The I.G.R.F

values used for this area are shown below.

-3000

-2500

-2000

-1500

-1000

-500

0

500

1000

0 1000 2000 3000 4000 5000

nT

Distance (m)

Without trend

With trend

36

Table 4.1 the I.G.R.F values for Kindani area

Component Field Value

Declination 0.67 degrees

Inclination -19.828 degrees

Vertical Intensity (Bz) 11455 nT

Total Intensity 31770 nT

Fig 4.8 (a) Euler solutions along profile AA’

From the profile AA’ (Fig. 4.8 a), Euler solutions suggest shallow magnetic structures at just

below the surface to a maximum depth of less than a km below the surface. The RTP curve

rises to its highest at a distance of 4000 m along the profile and is lowest at 1500m. The high

37

suggests a source of high magnetic susceptibility relative to host rocks while the low may

suggest rocks of lower susceptibility.

From profile BB’ (Fig. 4.8 b) solutions clusters occur near surface and between distances

750m to about 1750m along the profile. They also occur at 2250 m and between 4600 -

5000m along the profile. There is an abrupt change in vertical and horizontal gradients at

2250m. This also corresponds to an abrupt change in the RTP data curve outline. This point

also corresponds to one of the near surface Euler solution clusters. A discontinuity at a

distance of 4500m suggests presence of faulted structure.

Fig 4.8 (b) Euler solutions along profile BB’

38

On profile CC’ (Fig. 4.8 c), Euler cluster solutions occur less than 100m below the surface at

250m, 1000m and at 2500m along the profile. The deepest solution clusters occur at 530m.

These indicate presence of shallow magnetic structures which could be iron ore bodies. The

solutions at 250m and 1000m along the profile coincide with areas of abrupt changes in

horizontal and vertical gradients. These may represent abrupt lateral change in magnetization

relative to host rocks. Solutions at 4250m coincide with a point of inflection on the RTP

curve which may indicate the top of a magnetic body. There is also a rapid fall of both

vertical and horizontal magnetic gradients at 2500m. This indicates a rapid change in

magnetism relative to host rocks.

Fig 4.8 (c) Euler solutions along profile CC’

39

Fig 4.8 (d) Euler solutions along profile DD’

The Euler deconvolution solutions for profile DD’ (Fig. 4.8 d) indicates presence of magnetic

sources at a shallow depth of about 200m below the surface. The deepest sources occur at a

depth of 900m. These solutions occur at 800km, 2750m and at 3750m. These indicate

relatively shallowly buried magnetic bodies. The RTP curve dips deepest at 900m along the

profile and rises highest at about 2800m which indicates low and high magnetic

susceptibility bodies respectively.

40

4.4.3 Forward modeling results

Forward modeling was done using mag2dc software. The results obtained from Euler

deconvolution were used as start parameters in modeling. The program is based on Talwani

algorithm and allows manipulation of parameters like magnetic susceptibility, shape, and

depth until a best fit of calculated values to the observed values is obtained. The system uses

the method of least squares that gives a misfit value based on the differences between the

calculated and the observed values. In this study the misfit values were below 500 points for

all models. The results of the modeling are shown in figure 4.9 (a)-4.9 (d). The bodies are

labeled i, ii, or iii respectively from left to right in all the four profiles.

Fig 4.9 (a) 2-D Modeling results along profile AA’

The profile AA’ is about 4700 m in length and trends NW-SE of the RMI contour map. It

cuts through an elongated positive magnetic anomaly that has a NW-SE trend and also cuts

through a magnetic low on the southern part of the map. The models on the profile indicate

LEGEND: -------- Observed Calculated

41

two causative bodies with magnetic susceptibilities -1.204SI and 0.7284 SI respectively.

Body (i) is at a shallow depth of about 13m while body (ii) is at a modeled depth of 59m.

Body (i) is extensive covering a length of 3499 m while body (ii) has an approximate width

of 1274m. These bodies are speculated to be ferromagnetic subsurface bodies. The negative

susceptibility of body (i) could be as a result of reverse magnetization.

Fig 4.9 (b) 2-D Modeling results along profile BB’

Profile BB’ runs NW-SE at a bearing of 1460

to the North. It cuts through several magnetic

highs and lows in the Kindani study area. The profile runs about 5000m in length. Three

magnetic bodies were modeled along the profile. Body (i) occurs at the start of the profile

and has a body width of about 2244m. Its depth is estimated at 59m below the surface. Its

modeled susceptibility is -0.834SI. Body (ii) is an intrusive speculated to be an ore body of

LEGEND: -------- Observed Calculated

42

depth 112m below the surface. Its body width is modeled as 1300 m and its susceptibility is

1.7624 SI. Body (iii) has a magnetic susceptibility of -0.235SI. Its modeled body width is

1070m at a relatively shallow depth of 58 m.

Fig 4.9 (c) 2-D Modeling results along profile CC’

Profile CC’ has a SW-NE trend on the RMI map. It cuts through successive magnetic highs

and a low near its tail end. The profile runs a length of about 5500m, on a bearing of about

0520. Three causative anomalous bodies are modeled along this profile. These are speculated

to be iron ore bodies which are the sources of the highly magnetic surface rocks found in the

area. The 3 bodies (i), (ii) and (iii) have magnetic susceptibilities -1.204, -0.834 and 0.6305

LEGEND: -------- Observed Calculated

43

respectively. The three bodies are all relatively shallow at about 136m, 12m and 53 m

respectively. Their body widths are modeled as 533 m, 1926 m and 743 m respectively in

length. This indicates possible presence of extensive ferromagnetic ore bodies in the study

area.

Fig 4.9 (d) 2-D Modeling results along profile DD’

Profile DD’ (Fig. 4.9 d) cuts almost horizontally at the southern part of the map on a bearing

of about 860. It stretches about 4200m in length and cuts across the magnetic anomalies on

the southern part of the map. Two causative subsurface bodies are modeled on this profile.

Both bodies are near surface intrusives at 58m and about 0.1 m respectively, below the

surface. Body (i) stretches about 2037 m in length while body (ii) has a body width of about

1573m. Body (i) has a high magnetic susceptibility of 0.7284SI while body (ii) has a

LEGEND: -------- Observed Calculated

44

negative susceptibility of -0.235 SI. Both bodies are postulated to be magnetized iron bearing

ores. A summary of anomalous bodies’ properties is shown in the table 4.2 below.

Table 4.2 Summary of the 2-D modeling results

PROFILE BODY DEPTH TO TOP

OF BODY (M)

BODY

WIDTH

(M)

MODELED

SUSCEPTIBILITY

(SI)

AA’ i 13 3499 -1.204

ii 59 1274 0.7284

BB’ i 59 2244 -0.834

ii 112 1300 1.7624

iii 58 1070 -0.235

CC’ i 136 533 -1.204

ii 12 1926 -0.834

iii 53 743 0.6305

DD’ i 58 2037 0.7284

ii 0.1 1573 -0.235

45

4.5 Chemical analysis results

Four rock samples, from the Kindani survey area were presented for Chemical analysis. The

analysis was done by Energy Dispersive Spectroscopy (EDS/EDX). The samples were

sampled from 3 different regions of the study area. The samples are shown in the photos

below.

Fig 4.10 Rock samples from the Kindani study area

The map 4.10 indicates areas where the four samples S1, S2, S3 and S4 were collected.

Sample 4 was obtained by pitting while the rest are surface rocks. Looking at this map, and

reading it together with the elevation map, there doesn’t seem to be an influence of terraine

on the deposits.

21

S1

S2 S3

S4

4 cm

46

Fig 4.11 Areas where the rock samples were collected from

47

The results of the chemical analysis are as shown in table 4.3 below.

Table 4.3 Chemical analysis results of Kindani area samples

Element Sample 1 Sample2 Sample 3 Sample 4

Point picked/pit

Easting/Northing

389705

22873

390928

22805

390928

22805

389776

21617

Silicon as SiO2 % m/m 27.48 27.87 27.68 30.11

Iron as Fe2O3 % m/m 24.95 25.79 24.20 25.72

Aluminum as Al2O3 % m/m 22.74 25.35 23.83 22.21

Calcium as CaO % m/m 10.40 9.83 9.93 10.38

Potassium as K2O % m/m 8.78 5.02 8.83 8.77

Phosphorous as P2O5 % m/m 2.91 3.31 2.83 -

Titanium as TiO2 % m/m 2.03 2.13 1.96 2.09

The rocks found in Kindani are mostly ferromagnesian basaltic rocks of igneous origin.

Ferromagnesian silicates are minerals rich in iron and or/magnesium and typically low in

silica. Olivine, pyroxene, biotite and amphibole are common ferromagnesian constituents.

The mineral olivine is the most common in the Kindani rocks. Olivine , according to the

Bowen’s reaction series is one of the minerals that crystallizes first during cooling of

basaltic magma (Fredrick and Edward, 2000). This is then followed by crystallizing of

calcium rich plagioclase (CaAl2Si2O8). The chemical analysis results above (Table 4.3)

support presence of these compounds in the Kindani rocks.

The average crustal mineral composition of Si02 is 66%, Al2O3 at 15.2% and FeO at 4.5%

(Taylor and McLennan, 1985). In comparison, the silica composition of rocks in Kindani is

low at about 28%. In turn the values of Iron, aluminum and calcium are high. Titanium,

48

Phosphorous and Potassium are at about 2%, 3% and 8% respectively on average. Other

minerals like sodium are at negligible quantities.

The high quantity of iron suggests presence of the different ores of iron such as magnetite

(Fe3O4) and hematite (Fe2O3). The presence of the some titanium also suggests presence of

compounds like Ilmenite (FeTiO3) and ulvospinel (Fe2TiO4). Ilmenite usually is iron-black or

gray with a brownish tint (Heinz et al., 2005). Ilmenite is usually found in both igneous

rocks, such as those in Kindani and in metamorphic rocks. It usually occurs within the

pyroxenitic portions. Many igneous rocks contain grains of intergrown magnetite and

ilmenite usually formed by the oxidation of ulvospinel.

These elements also suggest presence of other different compounds. The high quantity of

aluminium suggests presence of the compound orthoclase (KAlSi3O8) and bauxite (AlOH3).

Bauxite usually occurs together with the iron oxides goethite and haematite, kaolinite and

anatase (TiO2). These lateritic bauxites were most likely formed by laterization of various

silicate rocks such as granite, gneiss, basalt, syenite and shale, most of which are present in

the Kindani area. This suggests that there has been significant weathering of the rocks in

Kindani area.

4.5.1 Comparison of Kindani with Kimachia values

Abuga et al. (2013) did a geophysical study in Kimachia area of Meru County. The chemical

analysis of some samples from the study area gave the results shown below. Since the two

regions studied are within the same county, and less than 100 km apart, it is important to do a

comparison of findings between the two areas.

49

Table 4.4 Chemical analysis results of selected samples from Kimachia area

Sample No 2445 2446 2449 2450

Iron as Fe2O3 92 86 11.8 22.5

Aluminum as

Al2O3

0.5 2.9 - -

Calcium as CaO 0.12 0.03 7.4 0.15

Potassium as K2O 0.01 Nd 2.4 5.8

Titanium as TiO2 0.44 0.4 1.2 0.76

MgO 0.01 0.03 2.7 6.8

Na2O 0.02 0.01 4.24 0.1

A comparison of the values from the two regions shows that samples from Kindani have an

average of 27-30% silica while the samples from Kimachia have silica values that range from

undetectable quantities in sample 2445 to high of about 57% in sample 2449. Kindani

samples have high values of potassium and Aluminium which have negligible values in the

Kimachia values. These quantities in Kindani suggest the compounds orthoclase (KAlSi3O8),

magnetite (Fe3O4), Ilmenite (FeTiO3) and possibly Calcium plagioclase (CaAl2Si2O8) as the

possible main compounds as earlier discussed. Magnetite seems to be the main compound in

the Kimachia samples. The two regions seem to have different rock formations. It is therefore

possible that iron mineralization occurred differently in the two areas even though they are

geologically close. It is evident that as Abuga et al., (2013) had speculated, the iron deposits

spread beyond the Kimachia area and cover other regions of the county. It is possible that

these deposits form a belt with the more extensive Tharaka deposits.

50

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions

A ground magnetic survey was effectively carried out covering an area of about 25km2. The

magnetic data was reduced for diurnal and geomagnetic corrections. Contour maps and 3-D

surface maps were used to qualitatively interpret the data. The contour map reveals an area

with extensive anomalies covering the entire study area, while the 3-D surface map gives a 3-

dimensional view of the magnetic variations within the study area. The major trend of the

anomalies is NW-SE and SW-NE trends. Four cross-sectional profiles were chosen cutting

across major anomalies in the study area. Euler deconvolution and 2-D forward modeling

was used to analyze the data quantitatively. Chemical analysis was also done by Energy

Dispersive Spectroscopy (EDS) on a few rock samples to quantify amount of Iron in the

samples. The following conclusions can be made from this work.

Euler deconvolution results reveal mostly near surface bodies interpreted as possible iron ore

deposits. The anomalous bodies are shallow, with the greatest depths of solutions noted at

about 1500m on profile DD’. These bodies are the sources of the highly ferromagnetic

surface rocks seen in Kindani area.

The high susceptibility values of up to 2.000SI modeled using Mag2dc software indicate high

magnetization of rocks in this area. Magnetization values are usually determined by the

amount of iron bearing minerals in a rock.

The Chemical analysis results confirm high quantities of iron (as Fe2O3) with up to 25% by

mass. These rocks were only a small sample and there is a likelihood of higher quantities in

other rocks within the area.

The 2-D models done using mag2dc software agree with the Euler solutions of mostly

shallow bodies, the lowest being less than 1m deep. The models show extensive bodies, the

51

longest being about 3500m. It is possible that these bodies are not a continuous ore, but

rather, several ore bodies lying along the profile. It might not have been easy to pick out

these discontinuities because a station spacing of about 500m was used. Perhaps this would

have been more obvious if smaller station spacing was used.

Ground magnetic surveying was therefore effectively used to delineate areas of possible iron

ore deposits within the Kindani area. Different analytic techniques have been used to

interpret observed data while employing geological constrains available for the area. The

Chemical analysis results were also important in confirming presence of iron content in the

area rocks. From their appearance and iron content, these samples don’t appear to be BIFs.

The iron ore in this region is most likely of magmatic origin.

5.2 Recommendations

Results of this study confirm presence of more iron ore deposits within Meru region than was

previously known. It is therefore necessary for the ministry of mining or some exploration

companies to do further exploration within the region, to quantify extent of coverage of these

deposits for possible exploitation. It is possible that the Kindani deposits extend within the

Meru national park, and therefore an aeromagnetic study might be necessary as ground

magnetics would be dangerous because of the wild animals.

It is also necessary to carry out more geophysical studies using a different method since

geophysical methods are usually non-unique. Electro-magnetics is recommended in this case

to delineate the deposits. This method is highly suitable since iron ions allow passage of

electric currents and therefore areas with these deposits would show enhanced conductivities

relative to their host rocks. Gravity methods may also be used since iron compounds usually

have high specific densities.

52

There is need to revise the geology of the area since geological reports available indicate that

it isn’t possible to find significant mineral deposits within the Meru region. This study,

coupled with other recent studies, suggest that these iron deposits could be significantly

extensive.

New surveys in the area should include accurate measurements of susceptibilities of rocks in

Kindani area as well as dip angles of these deposits. The instruments to measure these

quantities were not available at the time of this study.

53

REFERENCES

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56

APPENDIX I: RAW DATA & DIURNAL CORRECTIONS TABLE

STATION

SERIAL

EASTING NORTHING TIME OBSERVED

FIELD

DIURNAL

CORR.

RESIDUAL

ANOMALY

BS 389714 21367 1049 10295 0 10295

A1 392001 21119 1130 10318 -10 10308

A2 392494 21339 1147 10781 -14 10767

A3 393006 20985 1203 10883 -20 10863

A4 393535 20750 1224 10507 -23 10484

A5 393928 20818 1243 11139 -28 11111

BS 389714 21367 1409 10246 0 10246

A6 390999 21031 1420 10783 -45 10738

A7 390501 21003 1429 11308 -43 11265

A8 390000 21054 1437 10545 -42 10503

A9 389500 21198 1447 9967 -39 9928

A10 389000 21314 1457 10709 -36 10673

BS 389714 21367 1518 10262 0 10262

BS 389714 21367 915 10330 0 10330

B1 391500 24008 1008 11361 -8 11353

B2 391900 23733 1021 10761 -9 10752

B3 392500 23499 1037 10899 -10 10889

B4 393185 23499 1056 10046 -13 10033

B5 392900 23059 1113 9187 -16 9171

B6 393006 22679 1136 11154 -20 11134

B7 393226 22500 1144 11192 -21 11171

BS 389714 21367 1214 10307 0 10307

BS 389714 21367 1300 10272 0 10272

B8 391070 24138 1325 10797 -51 10746

B11 390600 23443 1420 10840 -44 10796

B12 391000 23013 1431 11852 -34 11818

B13 391500 23003 1449 10900 -28 10872

B14 392000 22807 1457 10844 -24 10820

B15 392444 22500 1505 11626 -23 11603

B16 393000 22004 1518 10544 -21 10523

B17 393400 21718 1529 10182 -15 10167

BS 389714 21367 1607 10323 0 10323

BS 389714 21367 927 10330 0 10330

C1 390500 23822 956 10728 -12 10716

C2 390504 24006 1005 10890 -18 10872

C3 390001 23702 1020 10768 -26 10742

C4 389298 23506 1038 11064 -32 11032

C5 390232 23500 1050 10762 -38 10724

BS 389714 21367 1102 10287 0 10287

C6 390000 23031 1126 10755 -38 10717

C7 389591 23044 1144 10720 -34 10686

C8 390500 22852 1204 10800 -29 10771

57

C9 391000 22928 1215 12028 -26 12002

C10 391500 22654 1223 10612 -25 10587

C11 391775 22500 1230 10501 -24 10477

C12 392000 22333 1237 11031 -23 11008

C13 392450 22066 1243 9469 -21 9448

C14 392888 22002 1300 10757 -18 10739

BS 389714 21367 1328 10318 0 10318

C15 390000 22027 1415 12046 -10 12036

C16 390500 22322 1428 11169 -9 11160

C17 390540 22177 1437 10536 -9 10527

C18 391000 22111 1448 10716 -8 10708

C19 391500 22013 1457 13119 -8 13111

C20 392041 21817 1505 10343 -7 10336

C21 392500 21714 1540 10517 -6 10511

BS 389714 21367 1617 10326 0 10326

BS 389714 21367 927 10308 0 10308

D1 390000 21215 945 10810 -16 10794

D2 390500 21445 954 12060 -20 12040

D3 391022 21456 1002 10013 -24 9989

D4 392000 21387 1016 9189 -38 9151

BS 389714 21367 1052 10244 0 10244

D5 389503 19003 1157 9625 -4 9621

D6 389606 19500 1206 10373 4 10377

D7 389655 20000 1215 9037 12 9049

D8 389481 20486 1225 10547 22 10569

D9 389580 21000 1236 10224 32 10256

BS 389714 21367 1241 10347 0 10347

BS 389714 21367 1330 10254 0 10254

D10 394044 18969 1422 10657 -32 10625

D11 394207 19553 1440 10764 -22 10742

D12 394118 19680 1448 10584 -20 10564

D13 394007 19951 1456 10041 -16 10025

D14 393684 20740 1512 10832 -8 10824

D15 393706 21000 1529 11315 0 11315

D16 393526 21158 1537 10512 2 10514

BS 389714 21367 1608 10326 0 10326

BS 389714 21367 904 10261 0 10261

E1 394025 20124 1003 10721 3 10724

E2 394144 20589 1013 9152 3 9155

E3 394185 21016 1028 11054 5 11059

E4 394124 21589 1044 10417 6 10423

E5 393955 22000 1054 10936 6 10942

E6 393759 21000 1123 11044 7 11051

BS 389714 21367 1149 10269 0 10269

58

E7 393221 21000 1230 10966 2 10968

E8 393062 20500 1240 10625 0 10625

E9 392876 20300 1255 11146 -2 11144

E10 392920 20500 1301 10592 -2 10590

E11 392614 21000 1318 10362 -4 10358

E12 392638 21500 1343 10026 -7 10019

E13 392601 21896 1354 10923 -9 10914

BS 389714 21367 1430 10249 0 10249

E14 391516 20000 1522 10149 21 10170

E15 391500 19833 1526 9526 23 9549

E16 391440 19500 1547 10144 35 10179

E17 391993 19034 1556 10545 42 10587

E18 392000 19904 1608 11251 48 11299

BS 389714 21367 1642 10331 0 10331

BS 389714 21367 922 10334 0 10334

F1 392448 20480 948 10617 1 10618

F2 392052 20008 959 10736 2 10738

F3 392471 20124 1009 10951 3 10954

F4 391775 20433 1025 11133 3 11136

F5 391784 20500 1032 10138 4 10142

F6 391445 20442 1047 10662 5 10667

F7 391390 22203 1058 10249 5 10254

F8 391323 20058 1108 10050 6 10056

BS 389714 21367 1122 10341 0 10341

F9 390849 20000 1149 10388 2 10390

F10 390770 20496 1209 12993 -2 12991

F11 390554 19501 1251 9828 -12 9816

F12 390733 19131 1302 11039 -14 11025

BS 389714 21367 1340 10311 0 10311

BS 389714 21367 933 10310 0 10310

G1 390513 20018 952 10376 -8 10368

G2 390117 20096 1006 10334 -12 10322

G3 390446 20530 1019 10397 -16 10381

G4 390498 19676 1038 9522 -22 9500

G5 390008 19509 1050 8434 -26 8408

G6 389749 19123 1111 11220 -34 11186

BS 389714 21367 1136 10269 0 10269

59

APPENDIX II: GEOMAGNETIC CORRECTIONS

STATION

SERIAL

EASTING NORTHING CORRECTED

VALUE

IGRF ANOM.

A1 392001 21119 10308 11454 -1146

A2 392494 21339 10767 11451 -684

A3 393006 20985 10863 11454 -591

A4 393535 20750 10484 11455 -971

A5 393928 20818 11111 11455 -344

A6 390999 21031 10738 11457 -719

A7 390501 21003 11265 11458 -193

A8 390000 21054 10503 11459 -956

A9 389500 21198 9928 11457 -1529

A10 389000 21314 10673 11456 -783

B1 391500 24008 11353 11421 -68

B2 391900 23733 10752 11423 -671

B3 392500 23499 10889 11425 -536

B4 393185 23499 10033 11424 -1391

B5 392900 23059 9171 11429 -2258

B6 393006 22679 11134 11435 -301

B7 393226 22500 11171 11435 -264

B8 391070 24138 10746 11421 -675

B11 390600 23443 10796 11429 -633

B12 391000 23013 11818 11434 384

B13 391500 23003 10872 11433 -561

B14 392000 22807 10820 11435 -615

B15 392444 22500 11603 11436 167

B16 393000 22004 10523 11442 -919

B17 393400 21718 10167 11445 -1278

C1 390500 23822 10716 11425 -709

C2 390504 24006 10872 11423 -551

C3 390001 23702 10742 11427 -685

C4 389298 23506 11032 11430 -398

C5 390232 23500 10724 11428 -704

C6 390000 23031 10717 11435 -718

C7 389591 23044 10686 11436 -750

C8 390500 22852 10771 11435 -664

C9 391000 22928 12002 11435 567

C10 391500 22654 10587 11436 -849

C11 391775 22500 10477 11437 -960

C12 392000 22333 11008 11439 -431

C13 392450 22066 9448 11441 -1993

C14 392888 22002 10739 11442 -703

C15 390000 22027 12036 11446 590

60

C16 390500 22322 11160 11442 -282

C17 390540 22177 10527 11443 -916

C18 391000 22111 10708 11444 -736

C19 391500 22013 13111 11444 1667

C20 392041 21817 10336 11446 -1110

C21 392500 21714 10511 11446 -935

D1 390000 21215 10794 11455 -661

D2 390500 21445 12040 11452 588

D3 391022 21456 9989 11451 -1462

D4 392000 21387 9151 11451 -2300

D5 389503 19003 9621 11481 -1860

D6 389606 19500 10377 11476 -1099

D7 389655 20000 9049 11470 -2421

D8 389481 20486 10569 11465 -896

D9 389580 21000 10256 11458 -1202

D10 394044 18969 10625 11474 -849

D11 394207 19553 10742 11467 -725

D12 394118 19680 10564 11466 -902

D13 394007 19951 10025 11464 -1439

D14 393684 20740 10824 11454 -630

D15 393706 21000 11315 11452 -137

D16 393526 21158 10514 11451 -937

E1 394025 20124 10724 11461 -737

E2 394144 20589 9155 11456 -2301

E3 394185 21016 11059 11451 -392

E4 394124 21589 10423 11444 -1021

E5 393955 22000 10942 11440 -498

E6 393759 21000 11051 11451 -400

E7 393221 21000 10968 11452 -484

E8 393062 20500 10625 11459 -834

E9 392876 20300 11144 11460 -316

E10 392920 20500 10590 11459 -869

E11 392614 21000 10358 11453 -1095

E12 392638 21500 10019 11448 -1429

E13 392601 21896 10914 11443 -529

E14 391516 20000 10170 11466 -1296

E15 391500 19833 9549 11469 -1920

E16 391440 19500 10179 11473 -1294

E17 391993 19034 10587 11477 -890

E18 392000 19904 11299 11467 -168

F1 392448 20480 10618 11461 -843

F2 392052 20008 10738 11465 -727

F3 392471 20124 10954 11463 -509

F4 391775 20433 11136 11461 -325

61

F5 391784 20500 10142 11461 -1319

F6 391445 20442 10667 11461 -794

F7 391390 22203 10254 11441 -1187

F8 391323 20058 10056 11466 -1410

F9 390849 20000 10390 11467 -1077

F10 390770 20496 12991 11462 1529

F11 390554 19501 9816 11474 -1658

F12 390733 19131 11025 11478 -453

G1 390513 20018 10368 11468 -1100

G2 390117 20096 10322 11467 -1145

G3 390446 20530 10381 11461 -1080

G4 390498 19676 9500 11471 -1971

G5 390008 19509 8408 11475 -3067

G6 389749 19123 11186 11479 -293

62

APPENDIX III: BASE STATIONS (BS) DATA USED FOR DIURNAL

CORRECTIONS AND DIURNAL CURVES

Day/Date Time

(Hours

Intensity

(nT)

Day 1

06/12/2014

1049 10295

1409 10246

1518 10262

Day 2

08/12/2014

915 10330

1214 10307

1300 10272

1607 10323

Day

310/12/2014

927 10330

1102 10287

1328 10318

1617 10326

Day 4

12/12/2014

927 10308

1052 10244

1241 10347

1330 10254

1608 10326

Day 5

13/12/2014

904 10261

1149 10269

1430 10249

1642 10331

Day 6

15/12/2014

922 10334

1122 10341

1340 10311

Day 7

16/12/2014

933 10310

1136 10269

10240

10260

10280

10300

10 11 12 13 14 15 16

Mag

net

ic In

ten

sity

(nT)

Time (Hrs)

Diurnal Curve Day 1

Intensity (nT)

63

10260

10280

10300

10320

10340

9 11 13 15 17

Inte

nsi

ty (n

T)

Time (Hrs)

Diurnal Curve Day 2

Intensity (nT)

10280

10300

10320

10340

9 11 13 15 17

Inte

nsi

ty (n

T)

Time (Hrs)

Diurnal Curve Day 3

Intensity (nT)

64

10220

10240

10260

10280

10300

10320

10340

10360

9 10 11 12 13 14 15 16 17

Inte

nsi

ty (n

T)

Time (Hrs)

Diurnal Curve Day 4

Intensity (nT)

10240

10260

10280

10300

10320

10340

9 11 13 15 17

Inte

nsi

ty (n

T)

Time (Hrs)

Diurnal Curve Day 5

Intensity (nT)

65

10300

10320

10340

10360

9 10 11 12 13 14

Inte

nsi

ty (n

T)

Time (Hrs)

Diurnal Curve Day 6

Intensity (nT)

10260

10280

10300

10320

9 10 11 12

Inte

nsi

ty (n

T)

Time (Hrs)

Diurnal Curve Day 7

Intensity (nT)

66

APPENDIX IV: PROFILE DATA

Profile Data for AA’

X (m) No trend W/trend

0.00 0.00 -1507.85

2.94 -0.24 -1507.69

5.41 -0.31 -1507.43

55.59 -2.33 -1502.72

108.25 -2.48 -1495.80

160.90 -1.05 -1487.30

213.56 0.99 -1478.19

266.21 1.59 -1470.53

318.87 -3.50 -1468.54

371.53 -22.90 -1480.88

424.18 -72.68 -1523.59

476.84 -171.21 -1615.06

529.49 -317.06 -1753.84

582.15 -491.18 -1920.89

634.80 -678.08 -2100.72

687.46 -867.02 -2282.59

740.11 -1045.61 -2454.11

792.77 -1192.93 -2594.37

845.43 -1277.08 -2671.45

898.08 -1277.31 -2664.61

950.74 -1213.45 -2593.68

1003.39 -1119.19 -2492.36

1056.05 -1017.12 -2383.21

1108.70 -919.43 -2278.46

1114.31 -910.11 -2268.39

1161.36 -830.81 -2182.77

1214.01 -755.99 -2100.89

1266.67 -696.61 -2034.44

1319.33 -649.95 -1980.71

1371.98 -567.43 -1891.12

1424.64 -453.24 -1769.86

1477.29 -354.32 -1663.87

1529.95 -273.99 -1576.48

1582.60 -212.06 -1507.47

1635.26 -167.51 -1455.86

1687.91 -139.01 -1420.29

1740.57 -125.15 -1399.36

1793.23 -124.66 -1391.81

1845.88 -136.43 -1396.51

1898.54 -159.33 -1412.34

1951.19 -192.06 -1438.01

2003.85 -233.03 -1471.90

2056.50 -280.28 -1512.09

2109.16 -331.51 -1556.24

2161.81 -383.78 -1601.45

2214.47 -432.63 -1643.23

2223.20 -438.61 -1648.04

2267.13 -468.92 -1672.45

2319.78 -477.19 -1673.66

2372.44 -434.81 -1624.20

2425.09 -336.91 -1519.24

2477.75 -205.86 -1381.12

2530.40 -61.93 -1230.13

2583.06 84.26 -1076.86

2635.71 227.26 -926.80

2688.37 362.97 -784.03

2741.03 486.11 -653.81

2793.68 588.18 -544.68

2846.34 657.90 -467.89

2898.99 689.44 -429.28

2951.65 690.91 -420.74

3004.30 677.38 -427.21

3056.96 659.94 -437.57

3109.61 643.81 -446.63

3162.27 630.54 -452.84

3214.93 620.04 -456.27

3267.58 611.65 -457.59

3320.24 604.59 -457.59

3332.10 603.11 -457.48

3372.89 597.92 -457.19

3425.55 591.09 -456.95

3478.20 583.46 -457.51

3530.86 574.39 -459.51

3583.51 563.29 -463.55

3636.17 549.60 -470.16

3688.83 532.89 -479.81

3741.48 512.84 -492.79

3794.14 489.34 -509.23

3846.79 462.46 -529.03

3899.45 432.48 -551.95

3952.10 399.78 -577.58

4004.76 364.84 -605.46

67

4057.41 328.10 -635.12

4110.07 289.99 -666.17

4162.73 250.82 -698.27

4215.38 210.83 -731.19

4268.04 170.12 -764.83

4320.69 128.69 -799.20

4373.35 86.40 -834.41

4426.00 42.99 -870.76

4441.00 30.20 -881.53

4476.51 -0.02 -906.99

Profile Data for BB’

X (m) No trend W/trend

0.00 0.00 -695.99

2.61 0.50 -695.49

40.72 8.38 -687.52

65.17 15.77 -680.07

127.74 34.87 -660.84

138.52 38.14 -657.55

190.30 59.62 -635.96

236.33 80.93 -614.54

252.86 91.53 -603.90

315.42 145.26 -550.03

334.13 167.93 -527.32

377.98 221.75 -473.41

431.93 296.86 -398.18

440.54 308.86 -386.17

503.10 402.80 -292.08

529.74 445.54 -249.28

565.67 503.48 -191.27

627.54 612.11 -82.51

628.23 613.29 -81.32

690.79 728.91 34.44

725.34 799.40 105.00

753.35 854.37 160.04

815.91 991.71 297.51

823.15 1008.18 313.99

878.47 1121.05 426.98

920.95 1217.76 523.79

941.03 1220.92 526.99

1003.60 1023.55 329.76

1018.75 979.50 285.75

1066.16 828.51 134.85

1116.56 680.47 -13.07

1128.72 644.29 -49.23

1191.28 466.47 -226.91

1214.36 405.23 -288.10

1253.84 300.37 -392.87

1312.16 156.93 -536.19

1316.40 146.75 -546.36

1378.97 6.59 -686.37

1409.97 -56.73 -749.63

1441.53 -116.95 -809.79

1504.09 -223.69 -916.38

1507.77 -229.42 -922.11

1566.65 -303.51 -996.07

1605.57 -345.28 -1037.75

1629.21 -354.94 -1047.37

1691.77 -348.20 -1040.48

1703.38 -337.11 -1029.37

1754.33 -173.37 -865.51

1801.18 202.38 -489.67

1816.90 321.84 -370.17

1879.46 956.66 264.79

1898.98 1170.67 478.84

1942.02 1506.11 814.38

1996.79 1721.87 1030.25

2004.58 1736.14 1044.54

2067.14 1584.52 893.06

2094.59 1452.49 761.09

2129.70 1301.42 610.09

2192.26 987.64 296.45

2192.39 986.96 295.78

2254.83 667.90 -23.15

68

2290.20 489.34 -201.63

2317.39 356.15 -334.77

2379.95 54.51 -636.27

2388.00 16.65 -674.11

2442.51 -234.17 -924.81

2485.80 -427.25 -1117.79

2505.07 -510.37 -1200.87

2567.63 -776.43 -1466.80

2583.61 -842.76 -1533.09

2630.19 -1030.92 -1721.15

2681.41 -1241.45 -1931.57

2692.76 -1283.81 -1973.91

2755.32 -1526.71 -2216.66

2779.21 -1509.38 -2199.28

2817.88 -1366.65 -2056.46

2877.02 -1129.50 -1819.19

2880.44 -1115.26 -1804.94

2943.00 -872.15 -1561.69

2974.82 -773.73 -1463.20

3005.56 -684.31 -1373.72

3068.12 -561.75 -1251.02

3072.62 -556.57 -1245.83

3130.69 -485.37 -1174.50

3170.42 -458.10 -1147.14

3193.25 -441.96 -1130.96

3255.81 -414.56 -1103.42

3268.23 -410.52 -1099.35

3318.37 -394.82 -1083.54

3366.03 -380.51 -1069.13

3380.93 -376.34 -1064.93

3443.49 -355.01 -1043.45

3463.83 -346.51 -1034.91

3506.05 -328.61 -1016.92

3561.64 -300.51 -988.70

3568.62 -296.84 -985.01

3631.18 -260.08 -948.11

3659.44 -241.83 -929.81

3693.74 -218.43 -906.33

3756.30 -169.94 -857.70

3757.24 -169.09 -856.85

3818.86 -110.91 -798.54

3855.05 -70.56 -758.11

3881.42 -41.08 -728.57

3943.98 36.18 -651.17

3952.85 47.29 -640.05

4006.55 117.23 -569.98

4050.65 171.38 -515.73

4069.11 195.35 -491.73

4131.67 258.34 -428.60

4148.46 269.27 -417.63

4194.23 293.47 -393.33

4246.26 302.25 -384.44

4256.79 301.69 -384.98

4319.35 290.52 -396.01

4344.06 284.30 -402.17

4381.91 269.93 -416.46

4441.87 247.07 -439.19

4444.48 245.88 -440.38

4507.04 218.46 -467.65

4539.67 204.79 -481.26

4569.60 191.23 -494.75

4632.16 164.44 -521.41

4637.47 162.25 -523.59

4694.72 137.84 -547.87

4735.28 121.64 -563.98

4757.28 112.72 -572.85

4819.84 88.93 -596.50

4833.08 84.19 -601.22

4882.41 66.60 -618.70

4930.88 50.72 -634.47

4944.97 46.21 -638.95

5007.53 27.54 -657.48

5028.69 21.71 -663.27

5070.09 10.75 -674.13

5114.22 0.02 -684.77

69

Profile CC’ Data

X (m) No trend W/trend

0.00 0.00 -1105.09

63.96 -10.29 -1114.81

73.55 -10.07 -1114.51

130.65 -26.19 -1130.12

159.34 -27.52 -1131.19

197.34 -35.87 -1139.21

245.13 -36.54 -1139.45

264.03 -38.60 -1141.34

330.71 -33.98 -1136.13

330.92 -33.94 -1136.08

397.40 -21.56 -1123.11

416.72 -14.83 -1116.21

464.09 2.52 -1098.44

502.51 23.91 -1076.71

530.78 41.17 -1059.19

588.30 89.87 -1009.98

597.47 98.43 -1001.34

664.16 178.37 -920.81

674.09 192.84 -906.25

730.84 283.32 -815.26

759.88 336.28 -762.04

797.53 410.12 -687.87

845.68 509.84 -587.72

864.22 550.15 -547.25

930.91 692.79 -404.00

931.47 693.93 -402.87

997.60 828.68 -267.52

1017.26 863.38 -232.65

1064.29 946.64 -148.97

1103.05 998.41 -96.86

1130.97 1036.50 -58.52

1188.84 1081.35 -13.15

1197.66 1088.43 -5.99

1264.35 1093.44 -0.39

1274.64 1086.43 -7.30

1331.04 1039.30 -53.93

1360.43 991.94 -101.03

1397.73 921.31 -171.33

1446.22 801.96 -290.25

1464.42 752.22 -339.83

1531.10 551.00 -540.45

1532.01 548.10 -543.34

1597.79 339.88 -750.98

1617.80 275.26 -815.42

1664.48 142.91 -947.35

1703.60 44.53 -1045.38

1731.17 -1.57 -1091.24

1789.39 -24.68 -1113.83

1797.86 -23.99 -1113.06

1864.55 66.81 -1021.67

1875.18 91.17 -997.21

1931.23 211.16 -876.73

1960.97 291.10 -796.53

1997.92 387.18 -700.11

2046.76 536.61 -550.24

2064.61 590.35 -496.35

2131.30 823.01 -263.09

2132.56 827.82 -258.27

2197.99 1085.27 -0.24

2218.35 1175.16 89.83

2264.68 1388.10 303.18

2304.14 1591.16 506.60

2331.36 1740.81 656.49

2389.93 2085.08 1001.28

2398.05 2139.55 1055.82

2464.74 2431.01 1347.88

2475.72 2424.56 1341.53

2531.43 2166.81 1084.27

2561.52 2014.99 932.72

2598.12 1767.77 685.82

2647.31 1509.68 428.18

2664.81 1417.94 336.59

2731.49 1135.86 55.10

2733.10 1130.21 49.47

2798.18 916.64 -163.52

2818.89 863.54 -216.44

2864.87 760.53 -319.03

2904.68 697.05 -382.16

2931.56 663.45 -415.52

2990.48 628.11 -450.34

2998.25 625.53 -452.85

70

3064.94 633.03 -444.75

3076.27 629.85 -447.84

3131.63 601.80 -475.39

3162.06 602.12 -474.80

3198.31 610.97 -465.63

3247.85 647.12 -429.03

3265.00 661.24 -414.76

3331.69 743.53 -331.87

3333.64 746.78 -328.61

3398.38 834.42 -240.39

3419.43 867.82 -206.80

3465.07 911.93 -162.29

3505.23 937.65 -136.22

3531.76 938.01 -135.61

3591.02 895.29 -177.81

3598.44 887.85 -185.18

3665.13 780.85 -291.59

3676.81 756.84 -315.49

3731.82 641.19 -430.65

3762.60 567.85 -503.72

3798.51 479.24 -592.01

3848.39 343.88 -726.93

3865.20 296.25 -774.40

3931.89 88.65 -981.41

3934.19 81.06 -988.98

3998.57 -146.16 -1215.63

4019.98 -228.53 -1297.80

4065.26 -412.37 -1481.24

4105.77 -592.27 -1660.78

4131.95 -711.73 -1780.01

4191.56 -1004.16 -2071.91

4198.64 -1029.60 -2097.29

4265.33 -1031.86 -2098.94

4277.35 -1016.82 -2083.80

4332.02 -884.77 -1951.26

4363.15 -828.50 -1894.71

4398.70 -756.98 -1822.88

4448.94 -674.98 -1740.44

4465.39 -647.45 -1712.75

4532.08 -553.29 -1618.00

4534.73 -550.12 -1614.81

4598.77 -470.68 -1534.80

4620.52 -448.17 -1512.10

4665.46 -401.10 -1464.62

4706.31 -364.91 -1428.07

4732.15 -342.17 -1405.10

4792.11 -297.04 -1359.43

4798.83 -292.07 -1354.41

4865.52 -248.56 -1310.31

4877.90 -241.55 -1303.18

4932.21 -210.99 -1272.14

4963.69 -195.58 -1256.45

4998.90 -178.56 -1239.12

5049.48 -157.09 -1217.19

5065.59 -150.36 -1210.32

5132.28 -125.62 -1184.99

5135.27 -124.63 -1183.96

5198.96 -103.57 -1162.34

5221.07 -97.12 -1155.70

5265.65 -84.22 -1142.40

5306.86 -73.64 -1131.45

5332.34 -67.17 -1124.75

5392.65 -53.52 -1110.56

5399.03 -52.09 -1109.08

5465.72 -38.58 -1094.97

5478.44 -36.27 -1092.55

5532.41 -26.53 -1082.33

5564.23 -21.41 -1076.93

5599.09 -15.84 -1071.05

5650.03 -8.57 -1063.32

5665.78 -6.34 -1060.95

5714.50 -0.02 -1054.20

71

Profile DD’ data

X (m) No trend W/trend

0.00 0.00 -1507.85

2.94 -0.24 -1507.69

5.41 -0.31 -1507.43

55.59 -2.33 -1502.72

108.25 -2.48 -1495.80

160.90 -1.05 -1487.30

213.56 0.99 -1478.19

266.21 1.59 -1470.53

318.87 -3.50 -1468.54

371.53 -22.90 -1480.88

424.18 -72.68 -1523.59

476.84 -171.21 -1615.06

529.49 -317.06 -1753.84

582.15 -491.18 -1920.89

634.80 -678.08 -2100.72

687.46 -867.02 -2282.59

740.11 -1045.61 -2454.11

792.77 -1192.93 -2594.37

845.43 -1277.08 -2671.45

898.08 -1277.31 -2664.61

950.74 -1213.45 -2593.68

1003.39 -1119.19 -2492.36

1056.05 -1017.12 -2383.21

1108.70 -919.43 -2278.46

1114.31 -910.11 -2268.39

1161.36 -830.81 -2182.77

1214.01 -755.99 -2100.89

1266.67 -696.61 -2034.44

1319.33 -649.95 -1980.71

1371.98 -567.43 -1891.12

1424.64 -453.24 -1769.86

1477.29 -354.32 -1663.87

1529.95 -273.99 -1576.48

1582.60 -212.06 -1507.47

1635.26 -167.51 -1455.86

1687.91 -139.01 -1420.29

1740.57 -125.15 -1399.36

1793.23 -124.66 -1391.81

1845.88 -136.43 -1396.51

1898.54 -159.33 -1412.34

1951.19 -192.06 -1438.01

2003.85 -233.03 -1471.90

2056.50 -280.28 -1512.09

2109.16 -331.51 -1556.24

2161.81 -383.78 -1601.45

2214.47 -432.63 -1643.23

2223.20 -438.61 -1648.04

2267.13 -468.92 -1672.45

2319.78 -477.19 -1673.66

2372.44 -434.81 -1624.20

2425.09 -336.91 -1519.24

2477.75 -205.86 -1381.12

2530.40 -61.93 -1230.13

2583.06 84.26 -1076.86

2635.71 227.26 -926.80

2688.37 362.97 -784.03

2741.03 486.11 -653.81

2793.68 588.18 -544.68

2846.34 657.90 -467.89

2898.99 689.44 -429.28

2951.65 690.91 -420.74

3004.30 677.38 -427.21

3056.96 659.94 -437.57

3109.61 643.81 -446.63

3162.27 630.54 -452.84

3214.93 620.04 -456.27

3267.58 611.65 -457.59

3320.24 604.59 -457.59

3332.10 603.11 -457.48

3372.89 597.92 -457.19

3425.55 591.09 -456.95

3478.20 583.46 -457.51

3530.86 574.39 -459.51

3583.51 563.29 -463.55

3636.17 549.60 -470.16

3688.83 532.89 -479.81

3741.48 512.84 -492.79

3794.14 489.34 -509.23

3846.79 462.46 -529.03

3899.45 432.48 -551.95

3952.10 399.78 -577.58

4004.76 364.84 -605.46

4057.41 328.10 -635.12

4110.07 289.99 -666.17

72

4162.73 250.82 -698.27

4215.38 210.83 -731.19

4268.04 170.12 -764.83

4320.69 128.69 -799.20

4373.35 86.40 -834.41

4426.00 42.99 -870.76

4441.00 30.20 -881.53

4476.51 -0.02 -906.99

73

APPENDIX V: ELEVATION DATA

S/SERIAL EASTING NORTH ING ELEV

BS 389714 21367 985

A1 392001 21119 870

A2 392494 21339 850

A3 393006 20985 854

A4 393535 20750 845

A5 393928 20818 802

A6 390999 21031 876

A7 390501 21003 910

A8 390000 21054 929

A9 389500 21198 966

A10 389000 21314 992

BS 389714 21367 985

BS 389714 21367 985

B1 391500 24008 870

B2 391900 23733 865

B3 392500 23499 866

B4 393185 23499 870

B5 392900 23059 865

B6 393006 22679 865

B7 393226 22500 858

B8 391070 24138 909

B9 390482 25038 943

B10 389985 25488 940

B11 390600 23443 946

B12 391000 23013 890

B13 391500 23003 897

B14 392000 22807 882

B15 392444 22500 840

B16 393000 22004 848

B17 393400 21718 820

C1 390500 23822 920

C2 390504 24006 935

C3 390001 23702 953

C4 389298 23506 1022

C5 390232 23500 920

BS 389714 21367 985

C6 390000 23031 1126

C7 389591 23044 1025

C8 390500 22852 910

C9 391000 22928 890

C10 391500 22654 897

C11 391775 22500 887

C12 392000 22333 881

C13 392450 22066 867

C14 392888 22002 835

C15 390000 22027 946

C16 390500 22322 910

C17 390540 22177 935

C18 391000 22111 905

C19 391500 22013 891

C20 392041 21817 869

C21 392500 21714 830

D1 390000 21215 940

D2 390500 21445 941

D3 391022 21456 922

D4 392000 21387 869

BS 389714 21367 985

D5 389503 19003 900

D6 389606 19500 931

D7 389655 20000 936

D8 389481 20486 950

D9 389580 21000 981

D10 394044 18969 775

D11 394207 19553 799

D12 394118 19680 805

D13 394007 19951 816

D14 393684 20740 838

D15 393706 21000 868

D16 393526 21158 820

E1 394025 20124 819

E2 394144 20589 821

E3 394185 21016 816

E4 394124 21589 830

E5 393955 22000 816

E6 393759 21000 833

BS 389714 21367 985

E7 393221 21000 840

E8 393062 20500 863

E9 392876 20300 862

E10 392920 20500 869

E11 392614 21000 865

E12 392638 21500 801

E13 392601 21896 832

E14 391516 20000 884

74

E15 391500 19833 886

E16 391440 19500 879

E17 391993 19034 857

E18 392000 19904 884

F1 392448 20480 877

F2 392052 20008 869

F3 392471 20124 843

F4 391775 20433 865

F5 391784 20500 865

F6 391445 20442 864

F7 391390 22203 878

F8 391323 20058 883

F9 390849 20000 889

F10 390770 20496 885

F11 390554 19501 902

F12 390733 19131 885

G1 390513 20018 902

G2 390117 20096 908

G3 390446 20530 912

G4 390498 19676 906

G5 390008 19509 918

G6 389749 19123 916