Document Type : Original Research Paper
Authors
1 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 University of Tehran
3 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract
This research applies an integrated data-driven weighting scheme combined with multi-criteria decision-making techniques for mineral potential mapping (MPM) to identify porphyry copper prospective areas within the Cenozoic Kerman magmatic arc, situated in the southeastern portion of Iran’s Urmia–Dokhtar magmatic belt. Four categories of exploration geospatial data—geological mapping, stream-sediment geochemical analyses, helicopter-borne geophysical surveys, and remote-sensing imagery—were collected and processed into thirteen evidential layers. These layers were subsequently integrated using four distinct decision-making frameworks: weighted index overlay, TOPSIS, VIKOR, and the isolation forest (IF) algorithm. In order to minimize subjectivity typically introduced through expert-based weighting of evidential layers, a data-driven prediction–area (P–A) plot approach was adopted, which jointly evaluates the proportion of known mineral occurrences relative to the favorable area occupied across multiple classified zones, derived through concentration–area (C–A) fractal analysis. The weights obtained through this approach were assigned to the respective evidential layers, ultimately generating copper potential maps for each of the methods. Because the same 20 known mineral occurrences were used both to derive the P–A-based weights and to assess the resulting maps using the P–A metrics and the Success-Rate Curve, these analyses represent non-independent, in-sample assessments of spatial concentration rather than independent validation or predictive testing on unseen occurrences. The P-A weight derived for Index Overlay, IF, TOPSIS, and VIKOR methods were found to be 1.39, 1.39, 1.39, and 1.45, respectively.
Keywords
Main Subjects
)