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Improved Wildcat Modelling of Mineral Prospectivity
Author(s) -
Carranza Emmanuel John M.
Publication year - 2010
Publication title -
resource geology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 43
eISSN - 1751-3928
pISSN - 1344-1698
DOI - 10.1111/j.1751-3928.2010.00121.x
Subject(s) - prospectivity mapping , geology , geochemistry , mining engineering , mineralization (soil science) , terrane , mineral , petrology , geomorphology , tectonics , seismology , soil science , structural basin , soil water , materials science , metallurgy
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.