Integrated remote sensing and geochemical studies for enhanced prospectivity mapping of porphyry copper deposits: A case study from the Pariz district, Urmia-Dokhtar metallogenic belt, southern Iran

Abstract

Mapping hydrothermal alteration zones associated with porphyry copper deposits (PCDs) is crucial for identifying new exploration targets on a regional scale. Hydrothermal alteration indicator layers play a fundamental role in recognizing potential areas for PCDs, highlighting the need for precise delineation of these zones and their integration with geochemical and geological data to reduce uncertainty in mapping porphyry copper prospectivity. This study focuses on the Pariz district within the Urmia-Dokhtar Metallogenic Belt (UDMB) in southern Iran, a region known for its significant porphyry copper mineralization. First, logical operator algorithms (LOA) were applied to ASTER remote sensing data to map and distinguish argillic and phyllic alteration zones associated with PCDs. Subsequently, propylitic alteration zones associated with chlorite-epidote and propylitic alteration associated with calcite were also delineated, as were silica-rich hydrothermal alteration zones. Five evidence layers corresponding to these geologic features were generated and weighted with logistic functions, independent of expert judgment and without consideration of the spatial distribution of known mineral occurrences (KMOs). In addition, two layers of information were developed, including multivariate geochemical signatures and proximity to intrusive rocks. The geochemical analysis identified two significant factors associated with porphyry copper mineralization: Factor-I (Zn, Pb, Cu, Sn, B) and Factor-II (Mo, Cu). These factors contributed to a multivariate geochemical signature in addition to the alteration layers derived from remote sensing. Evaluation using prediction-area (P-A) plots and Normalized density index (ND) confirmed the effectiveness of all seven layers for mineral prospectivity mapping (MPM). Geometric average (GA), data-driven index overlay (IO), and deep autoencoder neural network (DEA) integrated these layers, with IO showing superior performance in identifying high potential zones, as indicated by higher prediction rates compared to other methods. Therefore, IO proves to be the most efficient approach for mapping the regional porphyry copper minerals in the Pariz district of the UDMB.