Comprehensive modeling of mineral potential mapping by integration of multiset geosciences data

Abstract

This work has evaluated the functionality of various fuzzy-based fusion methods in the mineral potential mapping (MPM), by which a multi-criteria decision-making problem was solved to design a layout for drilling complementary boreholes through a comprehensive analysis of geospatial datasets. The novel methods employed were fuzzy c-means clustering, fuzzy gamma operator, fuzzy inference system (FIS), fuzzy outranking, and fuzzy ordered weighted averaging (FOWA). Kahang porphyry Cu-Mo deposit in the Isfahan province of Iran was chosen as a case study to examine the performance of these fuzzy methods in MPM. Extracted geospatial indicator layers for assessing the potential of porphyry-type mineralization were derived from four criteria, namely geology (rock units and faults), remote sensing (alteration map), geochemistry (Cu, Mo, and factor maps), and geophysics (reduced to the pole and analytical signal of magnetic data). The concentration-area multifractal method was utilized to reclassify each synthesized fuzzy favorability map into five classes. To appraise and compare the efficiency of each employed method, a productivity measure assumed as a cumulative summation of Cu grade multiplied by its thickness above an economical cut-off value of 0.2% was calculated along with each drilling (totally 33 ones). According to fuzzy favorability maps derived from running all fuzzy methods, the FIS and FOWA had the highest efficiency with 80 and 78% of accuracy, respectively. Eventually, taking all fuzzy maps into account led to the delineation of some new favorable zones, whereby further exploratory investigations are envisioned for determining their mining potential.