Document Type : Original Research Paper
Authors
1 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 University of Tehran
Abstract
This study presents a novel data-driven multi-criteria decision-making (MCDM) framework that integrates a hybrid Prediction-area (P-A) weight assignment technique with the Weighted Aggregated Sum Product Assessment (WASPAS) method to develop a robust gold prospectivity model for the Koodakan, Mokhtaran, and Basiran quadrangles in South Khorasan Province, Iran. The proposed framework effectively addresses two fundamental challenges inherent to supervised mineral potential mapping: the limited availability of known mineral occurrences and the inherent ambiguity in delineating barren terrains. A comprehensive suite of 18 evidential layers was compiled, encompassing lithological, structural, geophysical (magnetic, radiometric, resistivity), hydrothermal alteration, and geochemical (Au, Ag, Cu, Bi, Pb, Zn) datasets. The resulting WASPAS-based model exhibited superior predictive performance, attaining a high prediction weight of 1.51 and successfully capturing 82% of known mineral deposits within only 18% of the total study area. Benchmarking against the Multi-Index Overlay (MIO) method—which yielded a weight of 1.38 and identified 80% of deposits within 20% of the area—corroborated the enhanced efficacy of the WASPAS approach. Furthermore, validation using P-A plot analysis confirmed that WASPAS substantially reduces exploration risk and refines target prioritization, positioning it as a robust and efficient solution for prospectivity modeling in data-constrained geological settings.
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