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    Project ReportOn

    Application of GIS and Remote

    Sensing

    In Mineral Resources

    Completed under the guidance of Dr.Jasmeet Kaur

    (Professor of S.G.T.B Khalsa College)

    Submitted by:

    Hakim Asif Haider

    Life Science

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    ACKNOWLEMENT

    No significant achievement can be a solo performance

    especially when starting a project from ground up. This

    project on Application of GIS and Remote Sensing in

    Mineral Resources has by no means been an

    exception. Apart from the efforts of me, the success of

    this project depends largely on the encouragement and

    guidelines of many others. I take this opportunity to

    express my gratitude to the people who have been

    instrumental in the successful completion of thisproject.

    I would like to show my greatest appreciation to Dr.

    Jasmeet Kaur , Professor of S.G.T.B Khalsa College. I

    cant say thank you enough for Your tremendoussupport and help. I feel motivated and encouraged every

    time I attend her class. Without her encouragement and

    guidance this project would not have materialized.

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    The guidance and support received from my parents . I

    am grateful for their constant support and help.

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    Abstract

    The search for metals and materials needed to sustain

    our culture has been carried out since primitive man has

    searched for flint to craft hand tools. Today, the

    materials needed to drive our economic andtechnological growth are just as crucial. Most of the

    easily accessible metal ores were discovered decades

    ago; and thus the search has turned to more subtle

    deposits and more remote locations.

    Since the inception of rudimentary aerial photography

    at the turn of the twentieth century, remote sensing has

    been used as a tool in the search for economic mineral

    deposits. As the level of technology has improved, the

    value of remotely sensed data has increased. The page

    will highlight the history and implementations of

    remote sensing on mineral exploration today.

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    TABLE OF CONTENTS

    1) Acknowledgement......................................................................................22) Abstract.......................................................................................................43) Introduction..............................................................................................6-13

    A Pegmatite hosted gem mine in California. Pegmatites can often belocated within granite bodies due to differential vegetation growth

    and erosion patterns. This is prominent in aerial photos

    A fault trace near Moab Utah that would be difficult to detect on theground is easily seen in an aerial photograph.

    The Ray Rock Gold Prospect, North West territories, Canada. ALarge Scale Linear Feature related to an ophiolite sequence

    developed during Precambrian tectonism.

    4) GIS Approach In Mineral Targeting with Narayanpet Kimberlite SpatialDatasheet.........................................................................................................15

    a) Kimberlite Emplacement Model: Theoretical facts.........................16b)Narayanpet Kimberlite Field...........................................................20

    i) Geology........................................................................................21ii)Tectonic Elements........................................................................22iii)Geophysical Surveys.....................................................................22

    c) Project Work/ Methodology..............................................................23 i) Data Capture................................................................................24ii)

    Derivation of Theme Based Evidence Maps...............................30

    iii)Spatial analysis using Bayesian Probability Principle coupled withIndex............................................................................................33

    d)Conclusion.........................................................................................405) Mineral Prediction Using Remote Sensing and GIS in Rajasthan.......42

    a) Abstract ............................................................................................42b)Introduction......................................................................................44c) Objective...........................................................................................45

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    d)Materials and Methodology...........................................................46e) Study Area .....................................................................................47f) Result And Discussion....................................................................48 g) Conclusion......................................................................................52

    6) Summary.............................................................................................537) Bibliography.....................................................................................54-59

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    Introduction

    The value of remote sensing data to mineral

    exploration has evolved and increased as technology

    has improved. In the early days of aerial photography,

    aerial photos were used when available to evaluate

    topography and plan prospecting and sampling forays.

    After World War II, the analysis of aerial photo data

    became much more sophisticated and actualgeological data began to be extracted. The use of

    stereoscopic pairs enabled geologist to interpret subtle

    structural features. Nonetheless, the primary use of

    remotely gathered data was comparative. If a

    particular type of deposit was being mined in a

    district, aerial photos would be used to locate similar

    features elsewhere within the district.

    These trends of comparative photography

    continued until well into the satellite age when

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    satellite imagery became commercially available. The

    availability of multi-spectral, radar, and IR imaging,

    in variety of combinations allowed geologists to

    evaluate regions in much more detail than ever. In

    addition, the multiple flyovers allowed a prospect to

    be viewed in different light during different seasons.

    This greatly reduced the cost of regional explorationby precluding the need for repeated trips to a locale to

    reassess. Another advantage was the ability to gather

    data through cloud and surface cover with radar

    imagery. This allowed data to be collected from the

    tropics and arid regions that had previously been

    inhospitable to large regional field exploration. The

    computer age further enhanced the usefulness of data

    by allowing imagery to be digitally enhanced to

    highlight specific features. Now spectral studies can

    be done which allow the identification of specific

    minerals from space.

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    A Pegmatite hosted gem mine in California. Pegmatites can often be located

    within granite bodies due to differential vegetation growth and erosion patterns.

    This is prominent in aerial photos.

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    The most elementary operation of remote sensing in

    mineral exploration is using aerial photographs to

    identify topographic surface features which may imply

    the subsurface geology. Such telling surface features as

    differential erosion, outcropping rock, drainage

    patterns, and folds/faults can be identified. These

    features can be compared to other potential targets inthe region when looking for similar deposits. Faults

    fractures and contacts often provide a conduit or

    depositional environment for hydrothermal or magmatic

    fluids in regions of known mineralization, and thus

    make excellent targets for further investigation.

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    A fault trace near Moab Utah that would be difficult to detect on the ground is

    easily seen in an aerial photograph.

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    An Extension of simple air photo comparison is

    utilising satellite imagery to locate structural features on

    a regional level. This can allow geologists to narrow a

    large field of targets to a more manageable group. The

    same basic parameters exist, the search for favourable

    structural trends, only on a regional level. Field teams

    can than prioritise their energy in a more effective and

    efficient manner.

    The Ray Rock Gold Prospect, North West territories, Canada. A Large Scale Linear

    Feature related to an ophiolite sequence developed during Precambrian tectonism.

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    As Satellite image collection and data management

    improved a new kind of remote sensing application

    began being used by exploration geologists.

    Multispectral imaging and thematic mapping allowed

    surface mapping to be performed remotely in ways only

    dreamed about during the era of early photo

    interpretation. Different scanning spectrums enabled

    researchers to begin cataloguing various reflection and

    adsorption properties of soils, rock and vegetation.

    These spectra could be utilised to interpret actualsurface lithologies from remote images. With a field

    crew providing ground truth data, large areas could be

    geologically mapped in a short time at a fraction of the

    cost of traditional geologic mapping.

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    The Pogo district granitics, as seen from LandSat 5. These granites are

    clearly seen in this enhanced false color image. The Pogo district deposits

    are among the richest mineral locals in the world.

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    Diamond, as a lustrous precious stone, has human

    fascination since historical times. In India, well known

    epics the Ramayana and the Mahabharata have

    mention of ornaments of diamonds. India continued to

    have dominant position in diamond mining and trade till

    beginning of the 18th century. But, mining of the

    diamond on large scale started with real rush in the

    last quarter of the 19th century in South Africa. The

    rush in gradual process founded the basis for the

    modern scientific methods of exploration. Kimberlite

    was identified as the primary source.

    Index minerals were identified as exploration tools.

    Thermodynamics of diamonds and hostrock-mineral-

    entities and association helped in re-establishing the

    relation between the host rock and diamonds.

    Mineralogical assemblage, geophysical signatures and

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    geochemical attributes of the primary rock become the

    implements for integrated approach in prospecting.

    Airborne Surveys and remote sensed data were inducted

    in the search process. By now, there are sufficient

    numbers of discoveries world-over to establish the

    pattern of distribution in tectonic set-up and localisation

    in plate-tectonics model. The broad outlines used in

    exploration for primary source of diamonds can be

    summarised in the following lines:

    Kimberlite Emplacement Model: Theoretical facts

    World over, majority of the kimberlites are emplaced

    in ancient cratonic blocks (Clifford, 1966) or where

    Archaean basement is underlain by deep lithospheric

    keels (Haggerty, 1986).

    Productive or diamond bearing kimberlites are

    emplaced in areas where surface heat flow is generally

    less than 40 Mw/m2 .This condition is suitably available

    in majority of the cratonic blocks.

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    Ascent of Kimberlite melt is from upper mantle but

    below the graphite-diamond stability surface (i.e.,

    where pressure >45 to 60 Kb 150-200 km depth and

    temperature from 900 to 1300 C).

    The melt intrudes through pre-existing deep-seated

    faults / fractures, which have their roots in upper

    mantle.

    The deep-seated faults and deep-seated fractures are

    well depicted on the imageries in the form of

    lineaments traversing hundreds of kilometres.

    After reaching the lower level of the upper crust, the

    melt finds a number of shallow faults / fractures in its

    way. The melt intrudes into these weak zones of varied

    orientation in the form of cones and dykes.

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    The kimberlites are usually localised in zones of high

    magmatic permeability and repetitive basic and ultra

    basic magmatic activity (Kaminski et al., 1995).

    The kimberlites occur in clusters and such clusters

    make a field (of 1 to 50 or more bodies Janse, 1984,

    Mitchell, 1986) and a set of fields a province.

    Mantle-xenoliths and xenocrysts, along with

    diamonds, are inducted into the accelerated ascent of

    melt from the upper mantle environment and diamond

    stability zone.

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    The above stated facts could serve as conceptual model

    or exploration model in search of the locales of the

    primary sources for diamonds. A detailed account on

    conceptual facts in diamond exploration and

    identification of prioritised zones in India was dealt in

    the compilation work of Satyanarayana, 2000.

    With the advent of GIS technology with tessellation

    and vector encoding, the integrated approach in search

    of the primary sources for diamond also get a boost in

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    identification of statistically derived favourable zones

    from the overlaid predictive thematic layers.

    The present work constitutes an analysis for mineral

    targeting in 2953 sq km area falling in the Narayanpet

    Kimberlite Field, southern India, where integration of

    evidence maps is attempted on 'Index Overlay Model'

    coupled with 'Bayesian Probability Principle' in a

    vector GIS.

    Narayanpet Kimberlite Field

    The Geological Survey of India since 1984-85 has been

    discovering Kimberlite bodies in Maddur-Narayanpet

    area (Nayak et al., 1987: Sarma, 1990; Rao, 1995),

    Mahboob nagar district of Andhra Pradesh. Till date

    more than 30 Kimberlite bodies have been located, now

    this area which is designated as Narayanpet

    Kimberlite Field (NKF) (Satyanarayana et al., 1997)

    is a promising zone for kimberlites after the well-known

    Wajrakarur Kimberlite Field (WKF) in Dharwar

    Craton. The NKF measures about 60 km x 40 km in

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    western part of Mahboob nagar district, Andhra Pradesh

    and eastern part of the adjoining Gulberga district,

    Karnataka. The kimberlites of both the fields have

    broad similarities in mineralogical, petrological

    characters and major element chemistry, but there are

    subtle differences in REE distribution, indicator

    minerals and mantle nodules (Rao et al., 2001). Thekimberlites of both NKF and WKF are in similar

    tectonic set-up, and of contemporary ages (Anil Kumar

    et al., 1993).

    Geology

    The area comprises broadly Archaean gneisses,

    migmatites and granites with enclaves of schistose

    rocks, Proterozoic granitic intrusive and sediments of

    Bhma Group and Cretaceous Deccan Basalts.

    Emplacement of kimberlites of the NKF is noticed in

    the gneisses and granites (Rao et al., 1998). For

    prognostication purpose, the geology is simplified into

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    three litho domains i.e., gneissic domain, granitic

    domain and basalt domain.

    Tectonic Elements

    A number of faults, fractures and lineaments of varied

    dimensions, in different orientation related to different

    tectonic events are recorded in the area. Some of these

    are occupied by basic (including ultrabasic) intrusive,

    pegmatites, quartzo-feldspathic veins, younger acidic

    intrusive etc. These intrusive are suggestive of zones of

    magmatic permeability and repetitive basic and

    Ultrabasic magmatic activity in the area. The various

    tectonic elements of this area are grouped into E-W,

    NE-SW, N-S, NW-SE sets on the basis of their

    orientation.

    Geophysical Surveys

    Multidisciplinary studies involving detailed satellite

    imagery, aerial photo interpretation and ground

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    geophysical surveys were initiated in the NKF to locate

    new Kimberlite occurrences. Regional gravity and

    magnetic surveys carried out (Reddy et al., 2001) and

    interpretation of satellite and aerial photo data distincty

    brought out the co-axial relations existing between

    Geophysical anomaly linears and major lineaments /

    faults (Rao, 2001). The computer based dataset for the

    present work is derived from the analogue maps

    generated during the actual surveys and field activities

    in the NKF.

    Present Work

    Methodology

    The present GIS Project involves the following stages:

    1. Data capture from analogue maps

    2. Derivation of theme based evidence maps

    3. Spatial analysis using Bayesian Probability

    Principle coupled with Index

    Overlay Model for mineral targeting

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    Data Capture: Approach in search of new targets in an

    area is mostly based on the philosophy of known to

    unknown. In this respect the first step was to bring out

    all the appropriate data together into GIS database. The

    foresaid maps were digitised into GIS software

    Arc/Info version 8.2. The software captures data into

    workspace-coverage storage model.

    Two types of datasets i.e., evidence set and over

    plotting set, are delineated among the captured spatial

    dataset. The evidence spatial dataset comprising those

    discrete geographic objects evidencing the causativerole in the emplacement process or defining the

    signatures of probable locales of Kimberlite and allied

    rocks in NKF. And thus they are of predictor nature in

    Prognostication process. The spatial database from theevidence set are:

    i. Lithology

    ii. Structurefaults/fractures and lineaments

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    iii. Gravity contours interpreted too high and low axes

    represented as lines

    iv. Ground magnetic contours interpreted too high and

    low anomaliesrepresented as lines

    The other set represents the existing Kimberlite

    prospects in the NKF: The database of 33 discrete

    known bodies of Kimberlite and allied rocks.

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    The dataset was captured, populated with attribute

    values, assigned Polyconic projection with map-centre

    of the mosaic of four 1:50,000 topobases as origin of

    co-ordinate axes, edge-matched and mosaiced, thereby,

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    all the themes were brought to a common reference

    system. This facilitated overlaying and over plotting of

    spatial databases for GIS analysis. Study of over plotted

    digital maps is helpful in revealing the pattern of

    occurrence, field controls on known bodies and

    theoretical understanding of emplacement model for

    known bodies.

    Visual analysis in understanding the pattern of

    distribution of kimberlites in NKF aided in

    understanding the controls on Kimberlite emplacement.

    Small outcrops of geographic object like Kimberlite -represented by points on the digital map may have three

    fundamental distribution patterns i.e., a) Complete

    spatial randomness, b) Clustered pattern and c) Regular

    pattern. The kimberlites of WKF and NKF represent a

    clustered pattern. Each cluster has limited aerial spread

    And the spread of kimberlites of NKF is in the form of

    an ellipse. On the whole, the ellipse precisely defines

    the Kimberlite points. The major axis of this elliptical

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    distribution suggests a probable E-W control on the

    Kimberlite emplacement. But the ellipse has a

    considerably broad secondary axis perpendicular to the

    major axis, which suggests role of additional controls

    besides the E-W control.

    A)An ellipse define overall distribution of kimberlites of NKF B) Lithologicaldomains and distribution of kimberlites of NKF

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    Over-plotting of the Kimberlite occurrences on the

    generalised Lithological domains (comprising gneisses,

    diapiric granites and Deccan Basalt) brings out a

    modified distribution pattern influenced by the litho-

    domains. Though the number of known Kimberlite

    occurrences is almost equal in gneiss and granite (17

    and 16 respectively), the controls on distribution pattern

    in these two Lithological domains is attributed to

    different sets of faults. The major axis of orientation of

    the Kimberlite distribution is E-W in gneissic domain,

    whereas it is NNW-SSE in granitic domain.

    Most of the theoretical concepts of Kimberlite

    emplacement hold good for the NKF. The emplacement

    model for kimberlites emphasises - clustering in a form

    of nest of crustal faults and fractures in proximity of a

    mega lineament. The mega lineament in NKF is traced

    from the Cuddapah Basin to the east that extends

    hundreds of kilometres in E-W disposition. It is thus

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    Obvious that the linear geographic objects like faults,

    lineaments and gravity axes have controls on the pattern

    of distribution of kimberlites. The directional sets of

    feature-elements have different degree of controls, i.e.,

    from significant to least significant.

    Derivation of theme based evidence maps: GIS

    provides unlimited opportunities to make observations

    on over-plotted thematic maps, delineation of predictive

    features and understanding their role in identification of

    prognostication zones. Following the understanding of

    the cause and effect relationships, the second phase in

    an analysis process, is to manipulate the data so as to

    derive theme base evidence maps. Table 1 gives the

    details of the need for manipulation, the analysis toolapplied in spatial data manipulation and its objective as

    a part of prognostication over the Narayanpet dataset:

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    Table 1: Manipulation, a chain of operations to make

    the data usable in analysis process

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    A chain of operations through near, buffer and statistics

    reporting tools are utilised to establish the precisely

    defined proximity between the predictive features of

    different themes and locales of any economic

    consequence (target). 'Near tool' - provides opportunity

    to define the closest nearness in quantitative terms,

    between the predictive line features (for present studyfaults & lineaments and interpreted gravity linears) and

    the target point objects (kimberlites).

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    Spatial analysis using Bayesian Probability Principle

    coupled with Index Overlay Model for mineral

    targeting:

    Index overlay model (Bonham-Carter, 1996 and

    Westen, 1997) is selected for analysis with the

    Narayanpet evidence dataset. The procedure of the

    model is that each class of a predictive map is given

    different score as well as each predictive thematic map

    itself receives different weight. Before integration into a

    multi thematic map, each class or subset of a thematic

    map was weighted, ranking on a suitability scale (09,maximum 10 weighted classes). The spatial association

    between known occurrence and the predictive datasets

    are used to workout weights, which were applied to

    predictive areas with similar characteristics to the

    known occurrence.

    On account of standardising the weights for classes or

    sub-sets of a theme or for inter-thematic maps, factor (f)

    of posterior probability upon prior probability on

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    the notion of Bayesian Probability Principle were

    undertaken. Prior probability is the ratio of known

    kimberlites (33) upon area (NKF). The expression for

    prior probability is the unbiased distribution of

    kimberlites per sq km area. Meaning thereby, the factor

    of prior probability is an expression of predictability at

    a point in the area when none of the thematic controlsof predictive maps are taken into account. Whereas,

    posterior probability is the ratio between circumscribed

    Kimberlite bodies upon summed up area of the

    predictive zone (inside of the buffered zone). Thus, the

    posterior probability is a biased probability for a

    specific case. With the bias of known controls on the

    emplacement of kimberlites, there is always an

    improved degree of predictability for a point within the

    predictive zone. Assuming a common weight base (= 2)

    for the outside the predictive zone, the weight (integer)

    for inside is subscribed (Table-2) by multiplying weight

    base and probability factor (f). Thematic integration of

    subsets was carried out followed by integration into a

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    multi thematic map i.e., the subset-evidences together

    make a factor map. Addition of weights and

    normalisation was carried out in a newly created field in

    attribute table. Finally combining by union algorithms

    the three factor maps, namely-

    1. Fault and lineament factor map,

    2. Gravity factor map and

    3. Magnetic factor map result into the favourability map

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    * Prior probability of whole area or Dharwar Craton.

    # Relatively higher score is prescribed than the ratio.

    This is for highest upper crustal perforation along the

    set of faults (NW-SE) and the zone around has

    maximum number of Kimberlite localised.

    The favourability map generated on the combine of the

    Bayesian Probability Principle and Index Overlay

    Model, is a decision support map. The merit of the

    model could be realised when this map at decision

    making stage was over-plotted with Kimberlite

    occurrence map in ARCMAP of ARCGIS Version 8.2,

    for each selection of a set of polygons qualifying a

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    normalised weighted value or range of value one can

    have selection by location of the Kimberlite bodies

    falling within concerned polygons. A degree of

    confidence can be expressed in terms of known

    kimberlites falling within area acquired by the

    polygons. So, at a decision making stage, for each

    unique normalised weight classes the decision makerhave both figures - statistically summed up area and

    improved confidence level (Table-3, column-e).

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    For example, polygons selected for high range of

    weights (8 to 9) make patches, having a total area of 80

    sq km, which contain 3 known Kimberlite bodies in it.

    A higher ratio 3 nos/80 sq km (target area) upon 33

    nos/2953 sq km (project area) propose an improved

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    degree of confidence in the zone which is higher by

    3.37 times from prior probability.

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    Conclusion

    A chain of operations on near, buffer and statistics

    reporting tools are utilised to establish the precisely

    defined proximity between the predictive features of

    different themes and zones of any economic

    consequence.

    Using the proximity figures and sub-thematic weights

    resulted from proximity and probability driven analysis

    approach in working out a model for the Narayanpet

    Kimberlite Field could be applied on the whole to the

    Dharwar Craton for locating new nest of faults and

    fractures along mega-lineaments with any yield

    expectancy zones around them for kimberlites or related

    rocks. Most appreciating aspect of the model is the

    factor degree of confidence in a class or

    predictability of a class of the final favourability map,

    which prompts a prospector or a decision making

    authority to arrive at a right kind of decision. The

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    model is for close observation in light of new discovery

    in the NKF in recent years. In light of the fact that

    kimberlites of the NKF are in similar tectonic set-up

    with age-wise contemporaneous to WKF (~1100 Ma,

    Radiometric dating of the bodies), the model has a

    scope to improve and widen its application throughout

    the Dharwar Craton, which depicts heat flow regime of< 40 Mw/ m2 (Heat flow map of India, after Ravi

    Shanker, 1988) that is favourable for diamond stability.

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    Mineral Prediction using remote sensing and

    GIS in Rajasthan

    ABSTRACT

    The Indian state of Rajasthan is rich in mineral deposits

    including asbestos, mica, barite and talc. Integration of

    available background information (viz. geology,

    tectonics, mineralogy and geomorphology) in GIS

    environment leads to development of MineralExploration Geographic Information System. This study

    examines the use of remote sensing in geological

    studies in south east Rajasthan. Landsat Enhanced

    Thematic Mapper plus (ETM+) and TM were used to

    (i) classify the various geological units found in study

    area

    (ii) Discriminate the Lithology and structure of this

    area

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    (iii) Delineate the associated zones for mineral

    identification.

    A wide variety of digital image processing techniques

    were applied such as the principal components analysis.

    The colour composite of principal components (1, 2,

    and 3), enabled us to determine the different types of

    sedimentary rocks in the study area. The selective PCA

    of ETM+ using bands 1, 3, 4, and 5 was used in

    mapping iron and iron oxide bearing minerals. GIS can

    describe and analyse interactions, to make predictions

    with models and to provide support for decision-makers. Steps of mineral potential mapping includes

    identify mineralisation recognition criteria, data

    preparation and structuring, producing factor maps and

    combining factor maps in the appropriate inference

    networks. In this research, conventional models for

    combining factor maps have been investigated and GIS

    model selected in mineral deposit exploration in

    detailed stage. An integration model using of

    appropriate models has also been proposed.

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    INTRODUCTION

    Aravalli Mountain range hosts several economically

    viable mineral deposits. The mineral deposits found in

    this area include lead-zinc deposits, copper deposit,

    tungsten deposit and several other industrial mineral

    deposits. A careful study indicates a close association of

    most of the mineral deposits to the neighbouring

    fault/thrusts. Remote sensing (RS) and Geographical

    Information System (GIS) tools, mineral composite

    characteristics (ferrous minerals (FM), iron oxide (OI),and clay minerals (CM)) of south east Rajasthan were

    integrated and mapped. The resulting mineral

    composition index maps were summarised in nine

    classes by using natural breaks classification method

    in GIS. Using field data for which their geographic

    coordinates had been determined by global positioning

    system (GPS), developed MC maps were verified and

    found dependable for environmental studies.

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    OBJECTIVE

    The objectives were:

    To understand the geologic and geomorphic setting

    of study area.

    To understand image lineament and their

    importance in mineralisation zone.

    To understand the mineralisation pattern using

    remotely sensed data and GIS modelling.

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    Materials and Methodology

    Materials:-

    Spatial Data and Non- spatial data: Topographic

    sheet (scale 1:50,000 and 1: 2,50,000) and remote

    sensing data - IRS LISS III, SRTM data, thematic

    Mapper (TM), enhanced thematic Mapper (ETM+),

    geological maps and district resources maps.

    Software:ArcGIS 9.2, ERDAS IMAGINE, ILWIS,

    AutoCAD Map.

    Methodology:-

    The methodology is based on mineral composition

    and normalised difference vegetative (NDVI) indices

    (Tucker 1079; Sabins 1987; Jensen 1996; Campbell1996). Mosaicing is the first step for Landsat ETM+

    data and topographical data. The mosaic image was

    subset the area of interest by using vector AOI that

    was created on the map of S-E Rajasthan. Geological,

    geomorphologic, lineament, ferrous mineral, iron

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    oxide, clay minerals and mineral composition map or

    thematic layers was prepared. Then, weightages map

    of all layers for overlay analysis were prepared.

    Study Area:-

    The study area is located in the S-E part of Rajasthan.

    The main minerals present in the study area are Pb,

    Zn, Cu and gold. The study area (figure 1) extends

    between Latitude - 240 40 - 250 37 N and

    Longitude - 740 50 - 750 40 E. The covering area is

    7549.45 km.

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    RESULT AND DISCUSSION

    Geology: Rajasthan is endowed with a continuous

    geological sequence of rocks from the oldest Archaean,

    metamorphites, represented by Bhilwara Super Group

    to sub-recent alluvium and windblown sand. The

    sedimentaries include the rocks of Aravalli Super

    group, Delhi Super group, Upper Precambrian

    Vindhyan Super group and of Cambrian to Jurassic,

    Cretaceous and Tertiary ages. The south-eastern

    extremity of the state is occupied by a pile of basaltic

    flows of Deccan traps of cretaceous age (figure 2 (a)).

    Geomorphology: Geomorphology is the science of the

    evolution and the development of the landforms. It is

    concerned mainly with the study of the landforms and

    their genesis on the earths surface. The landforms are

    carved and/or developed on earth surface as a

    consequence of weathering and erosion and/or

    deposition.

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    Different types of landforms are available in the study

    area. These include alluvial plain, pediplain,

    denudational hill, dissected hill, plateau, residual hill,

    structural hill, valley fill and ridges (fig.2 (b)).

    Figure2(a) geological formation, (b) Geomorphological

    map of the study area

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    GIS modelling for minerals: The study of minerals is

    called mineralogy. It includes mineral identification and

    description, the classification of mineral groups and the

    study of where minerals occur. Minerals have an

    internal, crystalline structure. Molecules that make up

    the minerals are arranged in a set pattern. This

    crystalline structure is sometimes reflected as a crystalif the mineral is given room to grow. However, even

    organic substances can grow crystals (figure 4, 5).

    Figure 4. (a) Clay minerals (b) ferrous minerals of the

    study area

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    Figure 5. (a) Iron oxide (b) mineral composition map

    of the study area

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    CONCLUSION

    In this study, mineral composition raster maps of the

    study area were studied using remote sensing and GIS.

    The overall results indicated that a considerable amount

    of area has moderate iron oxide but poor ferrous and

    clay mineral contents. Negative correlations between

    iron (FM, IO) and clay (CM, clay %) variables

    suggested that the dominant clay minerals of the study

    area have low iron content (figure 6).

    The known mineral occurrences in SE Rajasthan are

    controlled by the Jahazpur Thrust and Great Boundary

    Thrust. The lineament length, lineament density and

    lineament intersection density, all these indicate NE-

    SW as dominant lineament trend in SE Rajasthan.

    Barite, steatite and tin are other important minerals

    present in SE Rajasthan. The techniques employed in

    the present study may be extended to prepare the

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    mineral potential map in other potential areas for

    mineral exploration programme.

    SUMMARY:-

    As population increases and more nations industrialize

    the demand for natural resources continues to increase.

    Growing pressure for environmental sustainability and

    the spread of population centres has driven the search

    for economically viable mineral deposits into more

    remote and desolate regions. Increased competitiveness

    and price consciousness from investors has produced

    intense pressure to maximize the return on exploration

    capital expenses. These are among the many factors

    contributing to the increased use ofremote sensing in

    geologic mineral exploration. As technology

    improves so will geologists ability to gather even more

    detailed data information from remotely sensed data.

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