APPLICATION OF GIS IN MINERAL EXPLORATION:Integration of multi-thematic dataM N MISHRASuperintending Geologist, CGMTGSI TI

GIS as a tool for decision makingAssembly of data in the form of a mineral potential map to decide priorities for future exploration

Evaluation of slope stability conditions to decide the best route for the construction of a new road

Investigation of spatial association between As in surface water and local geology

Etc. etc

Integration of spatial data from different sources to interpret the spatial phenomenon not apparent when individual spatial data is studied in isolation

Visualization of composite display

Creation of a new map from two or more maps

OverlaysBackground information on geology

Mineral occurrence map of the region

Metallogenic maps : forms of deposit, associated elements on geologic map

Historical maps, published / unpublished reports

Existing mines & prospects their economic status

Overlaying mineral occurrence map on geologic map to know the control of mineralization

OverlaysSynoptic studies by RS/ APRecognition of hydrothermal alteration zonesLandsat TM 4/7, 6/4, 7/4 BGR, SPOT, HyperspectralRadar data in vegetated areas

Regional geophysical studiesAirborne magnetics, radiometricsAirborne regional gravity surveysRegional seismic dataRefining geologic interpretationUnderstanding deeper crustal levelsRegional geochemical surveys

Exploration by Geophysical methodsConcealed ore deposit / concealed geologic bodiesInterpretation of geophysical anomaliesMagneticsElectromagneticsGravityRadiometrics : -ray, -rayResistivitySpontaneous polarization (SP)Induced polarization (IP)

MagnetotelluricsSeismicMise--la-masse

Points / Lines converted to polygons through Buffering / Thiessen Polygons etc

Exploration by Geochemical methodsChemistry of the environment surrounding a deposit to locate it in areas having poor outcropsDefining a geochemical anomaly which distinguishes the deposit from the background using the chemical characteristics of the depositGeochemical exploration program:PlanningSamplingChemical analysisInterpretationFollow up

Exploration by Geochemical methodsSoil, bed rock, stream sediment, heavy minerals, channel, trench samplingRegional reconnaissance geochemical samplingSystematic geochemical sampling in target areaKnowledge of elements associated with a particular deposit ex. VMS base metal deposits contain As & AuCr deposits have significant amounts of Ni, Fe, and MgPrimary dispersion: area of enrichment of some metalsRelative mobility of elements : secondary dispersion : Pb vs ZnGeostatistics

- NavigaSIG application : GIS Largest Deposits of the World

Developed by Laboratoire franco-russe de mtallognie

comprehensive database: 33 commodities, 1244 largest deposits

a tool designed to provide summaries in metallogenic thinking and to produce thematic decision-aid documents for rapidly assessing a regions potential

Complex queries possible

2D and 3D crustal modeling from seismic data

- 3D modeling of geological structures using geological, gravimetric, magnetic, and seismic data

mineral concentrations in a 4D space incorporation of a 4th, temporal dimension, representing the geodynamic and metallogenic evolution of the crustal volume considered

- data processing and data mining to characterize the different GIS objects, correlation search and quantification, and also classification techniques like neural networks which, coupled with a multi-criteria approach, make predictive tasks possible

Mineral Prospectivity MappingCollection, analysis, and integration of geological, geochemical and geophysical data to investigate the spatial distribution of anomalies

Anomalies may be indicator for mineralization

Integration of geo-exploration data traditionally done with light tables but at present can be done more efficiently in GIS

Spatial Modeling in Mineral Prospectivity MappingSpatial Modeling is the process of manipulating and analyzing spatial/geographic data to generate useful information for solving complex problems

Prescriptive & predictive modelsPrescriptive modelsinvolve the application of a set of criteria that are set out as good engineering practice, and may result from a blend of scientific, economic and social factorsExample: models used for the site selection process

Predictive modelsExample: mineral potential mappingthe ultimate purpose of determining mineral potential is to discover new deposits.

Spatial Modeling in Mineral Prospectivity MappingGIS Model:Output map = f (2 or more maps)The function f May be based on theoretical understanding of physical and chemical principalsMay be empirical, based on observations of dataMay be semi-empirical, e.g. slope processes and landslides

Spatial Modeling in Mineral Prospectivity MappingSince the physical and chemical principles governing the formation of mineral deposits is far too complex mineral prospectivity mapping is based on empirical models

Data-driven models calculated from training dataKnowledge-driven models estimated by experts (fuzzy logic)

Knowledge-driven models Binary evidence map with Boolean operations Binary evidence maps with Index overlay Index overlay with multi-class maps Fuzzy logic Data-driven models Statistics, regression, weights of evidence, Bayesian probability

GIS modeling for evaluating mineral potential

calculating mineral favorability from geoscientific maps by weighting and combining multiple sources of evidence weights based on the analysis of the importance of evidence relative to known mineral depositsor by using the subjective judgment of mineral deposits geologists

The Gadag Schist BeltForms the northern extension of the Chitradurga Schist Belt in Dharwar cratonComprises basic volcanic sequence at the base followed by sedimentary sequencesA number of thrusts exist parallel to the regional strike of the schist beltThe rocks are intruded by the younger Chitradurga Granite

The Gadag Schist BeltMineralization is mainly controlled by shear zones and also occurs along geological contacts Three parallel zones of mineralization presentIn the mafic sequence in the westAt the contact of the mafic rocks and the sedimentary rocks in the central partWithin the sedimentary rocks in the east

Exploration data setsGeological map (polygon)Lineament map (polyline)Faults (polyline)Electromagnetic (polygon)Aeromagnetic data (polyline)Heavy minerals (watershed data polygon)Stream sediment analysis (point)Mineral occurrences / deposits (point)

GEOFACGEOCHEMFACHEAVIESGEOPHFACPROSPECTIVITY MAPSTRUCTUREGEOLOGYSTREAM SED.ELCT.MAGNETICMAGNETICExploration data setsIntermediate Factor Maps

Creation of Evidence Maps

Classification of data and Generation of Evidence MapsGEOLOGICAL EVIDENCE MAP: 18 units reclassified to 5 units: GEO to GEOF

CLASSIFICATIONClassification to aggregate Lithounits into fewer categories

LithologyGeocodeRockType*DescriptionPink granite2011Igneous n metamorphicAmphibolite2021Igneous n metamorphicMetapyroxinite2031Igneous n metamorphicBiotite gneiss2121Igneous n metamorphicYounger granite2131Igneous n metamorphicChlorite phyllite2102Lowgrade metaSericite phyllite2252Lowgrade metaConglomerate2262Lowgrade metaBIF2802Lowgrade metaMetarhyolite2093Acid VolcanicQuartz porphyry2183Acid VolcanicArgillite2064SedimentaryGreywacke2354SedimentaryPillow basalt2145BasaltTuff2235BasaltMassive basalt2505BasaltSchistose basalt2515BasaltAmugdular basalt2525Basalt

Rock typeDescription1Gr, Gn, Amp, Pyx2BIF, Phy, Conglo3Rhy, Qz-Porphyry4Argillite, Gwackes5Basalt, tuff, basic rocks

Structural Evidence MapBuffering and merging of Lineament and Faults to get STRUBUF

Evidence map from Stream SedimentsSSCLSSAUSSAUSSASAuclass 1= Au 10 to 50Auclass 2= Au 50 to 100Auclass 3= Au >100Asclass 1= As 100 to 150Asclass 2= As 150 to 200Asclass 3= As >200

Evidence map from Heavy MineralsHMINALLGOLDALAuclass 1= AUA 1 to 25ALAuclass 2= AUA 25 to 50ALAuclass 3= AUA >50SCHclass 1= SCH 0 to 2SCHclass 2= SCH 2 to 5SCHclass 3= SCH>5ALLSULFSulfclass=1ALLSCH

Evidence map from Geophysical data setsEMEMBUF 500EMCLASS = 1 ELECTROMAGNETIC DATA

Evidence map from Geophysical data setsAEROMAGNETIC DATAMAGBUF

Magclass=1

INDEX OVERLAY METHOD

Creation of Factor Maps

Index Overlay MethodIn this method of modeling, the evidence layers are combined together to generate intermediate factor maps

Each input map or layer of evidence is assigned a weight depending upon its significance

Also, each class of every map is given a different score based on its relative importance

The weights are finally normalized with the combined map weights

Computation of weightings of different data sets

Factor map from the Union of Geology and StructureGEOF U STRBUF = U_GEO

Factor map from the Union of Geophysical data setsEMBUF U MAGBUF = U_GEOPH

Factor map from the Union of Stream Sediment dataSSAU U SSAS = U_GEOCHEM

Factor map from the Union of Heavy Mineral dataALLGOLD U ALLSULF U ALLSCH = U_HMIN

The Favorability MapThe four factor maps may now be combined to produce the favorability map

These factor maps may be assigned the following weights

Factor MapWeightU_Geo7U_Geoph2U_Geochem6U_HMIN6

The Favorability MapThe Favorability Map is the Union of all the factor mapsFAV_MAP = U_Geo U U_Geoph U U_Geochem U U_HMIN

Factor MapWeightU_Geo7U_Geoph2U_Geochem6U_HMIN6

Final Prospectivity Map

ValidationKnown mineral occurrences