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

Abstract

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, CO 2 , 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 -6008C, 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 highpotential 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.

Orogenic Gold Prospectivity Mapping Using Geospatial Data Integration, Region of Saqez, Nw of Iran

“…Demicco and Klir (2004) discuss the rationale and illustrate the applications of fuzzy logic modeling to geological studies but they do not provide examples of fuzzy logic applications to mineral prospectivity mapping. Recent examples of applications of fuzzy logic modeling to mineral prospectivity mapping are found in Hale (2001), Carranza (2002), Tangestani and Moore (2003), Ranjbar and Honarmand (2004), Rogge et al (2006) and Nykänen et al (2008). Typically, application of fuzzy logic modeling to knowledgedriven mineral prospectivity mapping involves three main feed-forward stages: (1) fuzzification of evidential data; (2) logical integration of fuzzy evidential maps with the aid of an inference network and appropriate fuzzy set operations; and (3) defuzzification of fuzzy mineral prospectivity output in order to aid its interpretation.…”

Reconnaissance-Scale Prospectivity Analysis for Gold Mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland

“…The effect of selecting different γ values is examined by Bonham-Carter (1994). Previous studies suggest γ values in the region of 0.9 are most suitable for prospectivity mapping (An et al 1991;D'Ercole et al 2000;Mukhopadhyay et al 2003;Tangestani and Moore 2003) although other studies have utilsed lower γ values of around 0.75 (Carranza and Hale 2001;Nykanen et al 2008;Porwal et al 2003). Choice of γ value is ultimately subjective and several were tested in this analysis.…”

Gold potential of the Dalradian rocks of north-west Northern Ireland: prospectivity analysis using Tellus data

“…This involves generalising the prospectivity map into a number of classes (e.g. very high, high, moderate etc) with equal intervals and measuring the distribution of the validation points across those map classes (Nykanen et al 2008). This methodology allows quantitative description of the distribution of targets and aids ranking for follow-up work.…”