Authors : Okeke Sunday Okechukwu; Okonkwo Churchill Chukwunonso; Achilike Kennedy Okechukwu

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/4n5yy9a8

DOI : https://doi.org/10.38124/ijisrt/26aug788

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Mineral exploration in covered and structurally complex terranes increasingly relies on the integration of complementary evidence layers rather than any single dataset. This study presents a reproducible, open-data workflow for mineral prospectivity mapping (MPM) that fuses multispectral and thermal remote sensing, potential-field and radiometric geophysics, and public geological and mineral-occurrence data within a common geographic information system (GIS) framework. The workflow combines knowledge-driven fuzzy logic, data-driven weights-of-evidence, and a machine-learning ensemble (random forest with gradient-boosted refinement) through a stacked meta-learner, and is validated using spatial k-fold cross-validation, receiver operating characteristic (ROC) analysis, and prediction-rate curves referenced against independent occurrence subsets. We demonstrate the framework on the Olympic Cu–Au Province of the Gawler Craton, South Australia — a world-class, largely covered iron oxide copper-gold (IOCG) system for which regional gravity, aeromagnetic, radiometric, magnetotelluric, and geological data are openly available through national and state geoscience portals. Evidence layers derived from reduced-to-pole magnetics, residual Bouguer gravity, radioelement ratios, ASTERderived alteration indices, structural lineament density, and lithological favorability are standardized to a common 30 m grid and integrated using the proposed fusion architecture. Consistent with published mineral-systems studies of the province, structural and magnetic-gravity evidence layers emerge as the strongest predictors of IOCG favorability, with alteration and radiometric layers providing secondary but non-redundant discrimination. We discuss the comparative advantages of ensemble fusion over single-source and single-method approaches, the sensitivity of prospectivity outputs to training-occurrence bias, and the practical steps required to operationalize the framework using entirely open datasets. The methodology, code structure, and evidence-layer catalogue are provided to support reproducibility and transfer to other covered metallogenic provinces.

Keywords : Mineral Prospectivity Mapping; Data Fusion; Remote Sensing; Potential-Field Geophysics; Machine Learning; Weights of Evidence; Fuzzy Logic; IOCG Deposits; Gawler Craton; Open Geoscience Data.

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