Mineral potential mapping in Central Iran using fuzzy ordered weighted averaging method

Abstract

ABSTRACTThe aim of this work is to introduce the application of the fuzzy ordered weighted averaging method as a straightforward knowledge‐driven approach to explore porphyry copper deposits in an airborne prospect. In this paper, the proposed method is applied to airborne geophysical (potassium radiometry, magnetometry, and frequency‐domain electromagnetic) data, geological layers (fault and host rock zones), and various extracted alteration layers from remote sensing images. The central Iranian volcanic–sedimentary belt in Kerman province of Iran that is located within the Urumieh–Dokhtar (Sahand–Bazman) magmatic arc is chosen for this study. This region has high potential of mineral occurrences, especially porphyry copper, containing some active world‐class copper mines such as Sarcheshmeh. Two evidential layers, including the downward continued map and the analytic signal of such filtered magnetic data, are generated to be used as geophysical plausible traces of porphyry copper occurrences. The low values of the resistivity layer acquired from airborne frequency‐domain electromagnetic data are also used as an electrical criterion in this study. Four remote sensing evidential layers, including argillic, phyllic, propylitic, and hydroxyl alterations, are extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer images in order to map the altered areas associated with porphyry copper deposits. The Enhanced Thematic Mapper plus images are used to map iron oxide layer. Since potassium alteration is the mainstay of copper alteration, the airborne potassium radiometry data are used. Here, the fuzzy ordered weighted averaging method uses a wide range of decision strategies in order to generate numerous mineral potential/prospectivity maps. The final mineral potential map based upon desired geo‐data set indicates adequately matching of high‐potential zones with previous working mines and copper deposits.