A knowledge-guided fuzzy inference approach for integrating geophysics, geochemistry, and geology data in a deposit-scale porphyry copper targeting, Saveh, Iran

The main aim of this study is to investigate the applicability of a Fuzzy Inference System (FIS) approach to produce a copper mineral potential map (MPM) at the Saveh area in the Markazi province of Iran. Seven indicator layers extracted from the geological, geochemical, and geophysical data sets are casted in a geospatial database for data integration. Indicator layers are rock types, alterations, Cu concentration anomaly, main geochemical principal component anomaly, reduced-to-pole magnetic data, electrical chargeability, and electrical resistivity. A fuzzy gamma operator is used at the first phase of exploratory data integration to produce three criterium layers that are geology, geochemistry, and geophysics. Then, at the second phase, the FIS is implemented in three main stages consisting of: 1) fuzzification of input/output data, 2) designing an inference engine, and 3) defuzzification of integrated data. The mineral favourability map is prepared and reclassified into five zones through a multifractal approach at the third phase. In order to evaluate the accuracy of the FIS method, the productivity index of 18 boreholes are utilised to examine the correlation between the mineralised zones and the MPM output. Whereby the synthesised indicator layers demonstrated a Pearson's linear correlation coefficient of 0.44 in recognising copper mineralisation at depth. In addition, the eastern and central portions of the Saveh prospect were proposed as favourable potential zones for further mining operation.