A Computational Optimized Extended Model for Mineral Potential Mapping Based on WofE Method

Abstract

The multivariate fuzzy-c-means classifier is used to model extended weight of evidence (WofE) considering predictor maps. Approaches to mineral potential mapping based on WofE modeling generally use binary maps, whereas, real-world geospatial data are mostly multi-class or fuzzy-class in nature. The consequent reclassification of fuzzy-class maps into binary maps is a simplification that might result in a loss of information. This research thus describes an extended WofE modeling for predicative mapping of gold deposit potential in Tourd-chah Shirin metallogenic zone, Semnan province, in north of Iran to demonstrate optimization of mineral potential information by using fuzzy-class predictor maps, as applied to the study area. The optimization of an extended WofE model using fuzzy-class predictor maps for the study area results in demarcation of the high, moderate and low favorability zones. Optimization was also obtained by constraining simple WofE model using only binary predictor maps with different levels of uncertainty for study area. A comparison between the results of the extended WofE model and field data indicates that little correlation exists between these two results.