Hybrid outranking of geospatial data: Multi attributive ideal-real comparative analysis and combined compromise solution

Abstract

Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (Nd) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (Nd = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (Nd = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study.