A deposit scale mineral prospectivity analysis: A comparison of various knowledge-driven approaches for porphyry copper targeting in Seridune, Iran

Abstract

In this paper, an application of a knowledge-driven mineral prospectivity mapping (MPM) approach so-called “the evidential belief functions (EBFs) using Dempster-Shafer's rule of combination” is proposed. This technique is used to weight and integrate a large scale exploration dataset in order to localize prospects for definition of further exploration drilling sites. In this study, exploration datasets of Seridune copper deposit in the Kerman province, SE Iran used for the methodology. In this regard, geophysical evidence layers extracted from interpretation of magnetic and electrical surveys, geological evidence layers derived via the geological datasets (i.e. lithology, fault and alteration), and geochemical evidence maps were generated and integrated for MPM. Furthermore, various MPM approaches including outranking, index overlay and fuzzy logic methods were examined for comparison with the introduced method. To evaluate and compare the efficiency of the methods, the productivity of the drilled boreholes (Cu concentration multiplied by its ore thickness along each drilled borehole) was used to validate the generated prospectivity models. The results showed higher efficiency of the Dempster-Shafer's model in comparison with the prospectivity models generated using other MPM approaches.