Copper potential mapping in Kerman copper bearing belt by using ANFIS method and the input evidential layer analysis

Abstract

Earth science information used in mineral potential mapping has an empirical component comprising an exploration database and a conceptual component comprising an expert knowledge base. The hybrid neuro-fuzzy model combines conceptual and empirical components of available earth science information for predictive mineral potential mapping effectively. This paper describes a neuro-fuzzy model, which combines exploration data in the regional scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps are in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration, and geochemistry. The modeling result was 1044 pixels selected as favorable in order to continue the copper exploration in the study area; in other words, approximately 11.7 % of the area was selected. Fifty six known deposits out of 86 ones, equal to 65 % of all, were located in favorable zone. Other main goals of this study were to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data with its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer has a certain behavioral pattern. These behavioral patterns can be considered as regional copper exploration criteria.