Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran

This research focused on mineral prospectivity mapping (MPM) for porphyry copper deposits in the Varzaghan area of northwestern Iran. We investigated the use of the Analytic Hierarchy Process (AHP), a multiple-criteria decision-making (MCDM) tool, within a knowledge-driven MPM framework. To address potential bias and uncertainty introduced by expert opinions, we implemented a novel approach using the prediction-area (P-A) plot to optimize the construction of the pairwise matrix. Our data-driven AHP model integrated multi-element geochemical data, fault density, proximity to intrusive rocks, and distances to argillic and phyllic alterations. We validated the model using 17 known mineral occurrences (KMOs) as benchmarks. The P-A plot assessment showed that 80% of KMOs were predicted within 20% of the study area, demonstrating the effectiveness of the data-driven AHP method for identifying exploration targets in the Varzaghan region.