Improved Wildcat Modelling of Mineral Prospectivity

Wildcat modelling of mineral prospectivity has been proposed for greenfields geologically-permissive terranes where mineral targets have not yet been discovered but a geological map is available as a source of spatial data of predictors of mineral prospectivity. This paper (i) revisits the initial way of assigning wildcat scores (S_c) to predictors of mineral prospectivity and (ii) proposes an improvement by transforming S_c into improved wildcat scores (IS_c) by using a logistic function. This was shown in wildcat modelling of prospectivity for low-sulphidation epithermal-Au (LSEG) deposits in Aroroy district (Philippines). Based on knowledge of characteristics of and controls on LSEG mineralization in the Philippines, the spatial predictors of LSEG prospectivity used in the study are proximity to porphyry plutonic stocks, faults/fractures and fault/fracture intersections. The S_c and IS_c of spatial predictors are input separately to principal components analysis to extract a favourability function that can be interpreted as a wildcat model of LSEG prospectivity. The predictive capacity of the wildcat model of LSEG prospectivity based on the IS_c of geological predictors is roughly 70% higher than that of the wildcat model of LSEG prospectivity based on the S_c of geological predictors. A slight increase of predictive capacity of wildcat modelling of LSEG prospectivity is also achieved when the IS_c of geological predictors are integrated with the IS_c of geochemical anomalies, but not with the S_c of geochemical anomalies. The proposed improvement is significant because if the study district were a greenfields exploration area, then a wildcat model of LSEG prospectivity based on the old wildcat methodology would have caused several LSEG targets to be missed.