Integrating exploration dataset in GIS using fuzzy inference modeling  Basab Mukhopadhyay  Niladri Hazra  M K Mukhopadhyay Geodata and Database Division Geological Survey of India, CHQ, Kolkata gsi_chq@vsnl.com The Singhbhum Copper Belt, located in Jharkhand state in Eastern India, forms an arcuate linear zone (Fig.1) in the Precambrian Singhbhum Crustal Province (PSCB), houses over 250 million tonnes of copper ore of variable tenor and considered one of the most potential copper sulphide bearing stretch of the country. It is found that only 15% of the total 128 Km stretch of the copper belt shear zone between Bhatin and Badia yield maximum number of economic copper deposits. Five decades of exploration by GSI has generated huge inventory of spatial and related attribute data which need to be relooked again to find out new locales for further exploration, as ore reserves remained stagnant in this belt over a decade. Geological knowledge of an area may play the most important role, if not the only one, in finding out the mineral potential. But unfortunately, in most of the cases, this is a qualitative classification which cannot be transmitted in the modeling environment of GIS. A qualitative spatial representation between geological features in terms of predictive mineral potential model (taking into consideration the nature of known mineralisation), is by far the most vital part in any analysis. The quantified spatial association between different themes based on exploration model and copper occurrences in PSCB, has already been quantified in an earlier publication (Mukhopadhyay et. al., 2002) by an intuitive knowledge driven approach. Same data layers and weight scheme have been used in this analysis to calculate values for fuzzy membership function and combined by fuzzy logic as inference engine. 
GIS Modeling In the classical GIS modeling, the process converts the multiclass maps into binary predictor pattern. The pattern assumes a boundary between favourable and unfavourable ground (Carranza and Hale, 2000). However, the boundary between these two classes, is imprecise and thus fuzzy (Carranza and Hale, 2001). Hence, classifying predictor maps on the basis of their mineral favourability needs a concept which takes care of the favourability zones on a gradational basis rather than simply classifying them into classes of membership or non membership. As fuzzy set is expressed on a continuous scale from 1 (full membership) to 0 (full non membership) (Bonham-Carter, 1994), the inference engine generates map on a gradational pattern depending on the fuzzy membership value, which also takes into consideration the probability and possibility of finding mineral potential in actual ground. In the fuzzy system, the extraction and combination of different evidences, is carried out by operators. An.p., et. al. (1991) discussed five operators, which are found useful for combining the exploration dataset. These are fuzzy and, fuzzy or, fuzzy algebric product, fuzzy algebric sum and fuzzy gamma operators. Out of these five operators: the last four operators have been used which deserve elaboration. (Bonham –Carter, 1994).
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