Spatial modeling for base-metal mineral exploration through integration of geological data sets

This study involves the integration of information interpreted from data sets such as Landsat TM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura-Dariba, Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights of evidence method and (2) a fuzzy logic algorithm to derive spatial models to target potential base-metal mineralized areas for future exploration. Of the 24 layers considered, five layers (graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0-2000 m corridor (L4-NESW), Cu 200-250 ppm, and Pb 200-250 ppm) have been identified from the Bayesian approach on the basis of contrast. Thus, unique conditions were formed based on the presence and absence of these five map patterns, which are converted to estimate posterior probabilities. The final map, based on the same data used to determine the relationships, shows four classes of potential zones of sulfide mineralization on the basis of posterior probability. In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SW lineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain the final potential map showing four classes of favorability index. © 2000 International Association for Mathematical Geology.