Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland

Abstract

Despite its exploration immaturity, the Central Lapland Greenstone Belt has geodynamic, geological and gold-endowment characteristics to indicate that it is potentially the premier orogenic-gold province in Finland. Previous GIS-based prospectivity mapping of the Central Lapland Greenstone Belt used only empirical geophysical and geochemical datasets based on the weights-of-evidence integration method. In this study, such empirical data are combined with conceptual geological parameters derived from a 1:200 000 scale bedrock map of northern Finland and use the weights-of-evidence, logistic regression and fuzzy-logic methods for integration. A targeting model is developed based both on the empirical data and conceptual parameters defined from understanding of the processes essential to produce large deposits in an orogenic-gold mineral system. Key spatially referenced layers are derived from the empirical data and from critical proxies for essential processes in the targeting model, and the spatial association between these key layers and a training set of known gold deposits and prospects is quantified. Key parameters are then reclassified into binary layers according to maximum spatial association with the training set, and these layers are then integrated into prospectivity maps using both the weights-of-evidence and logistic regression methods. The highest probability class in all models defines <1% of the Central Lapland Greenstone Belt, a remarkable reduction in highest-priority target zones. The resulting maps are evaluated using a set of gold prospects excluded from the original training set as validation sites. The resultant prospectivity maps provide subtle, but significant, improvement on the maps derived solely from empirical data, especially where logistic regression is applied. Proxies for strain gradients appear particularly important. The analysis demonstrates that homogeneous, high-quality, small-scale geological maps, integrated using a robust targeting model, can enhance the predictive capacity of prospectivity maps derived from GIS-based modelling.