Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper-gold deposits in the northern Fennoscandian Shield, Finland

The conceptual approach used in this study incorporates spatial analysis techniques for data integration and analysis to perform reconnaissance-scale mineral prospectivity mapping for iron oxide copper-gold (IOCG) mineralisation in Finland. The known IOCG occurrences in Finland are characterised by the following features: (i) an epigenetic magnetite-rich host-rock; (ii) an association of Fe-Cu-Au +/- Co +/- U; (iii) ore minerals comprising magnetite, chalcopyrite, pyrite or pyrrhotite, and native gold; (iv) a gangue dominated by Ca-amphibole +/- diopside, albite and biotite; (v) enrichment in Ag, Au, Bi, Ca, CO2, Cu, Fe, S, Te +/- As, Ba, Cl, Co, K, LREE, Mo, Na, Pb, Rb, Sb, Se, U; ( vi) multi-stage alteration; (vii) formation in the P-T range of 400-600 degrees C, 150-350 MPa; and (viii) a distinct structural control in regions that have experienced both extensive compression and extension. The datasets used for the prospectivity analysis include a 1:1 000 000 scale geological map, high-resolution airborne geophysics, regional-scale multi-element till-geochemistry data, and a mineral occurrence database. The derived parameters used in the conceptual analysis include: (i) proximity to the craton margin; (ii) intersecting fault structures; (iii) presence of granitic intrusions particularly those with compatible and incompatible element enrichment; (iv) Cu, Co and Fe concentrations in till samples; (v) presence of hematite; and (vi) airborne magnetic highs and radiometric U data. A conceptual fuzzy-logic model was used to predict and locate the most prospective or favourable areas for IOCG exploration in the study area using the above-mentioned data layers. The models identify several permissive and high-potential areas within a significantly reduced potential exploration area. Validation of the modelling was conducted by quantifying the spatial association between the predicted endowment as favourability classes on the prospectivity map and the known mineral deposit sites with IOCG affinities using the Bayesian weights-of-evidence method.