Regional-scale nickel sulfide prospectivity mapping of the Yilgarn Craton, Western Australia

Abstract

Bayesian weight-of-evidence and logistic regression models are implemented in a GIS environment for regional-scale prospectivity mapping of nickel sulfide deposits in the Yilgarn Craton, Western Australia. The input variables for the models consisted of GIS layers that were used as proxies for the mappable exploration criteria of nickel sulfide deposits in Yilgarn. About 70% of the 169 known deposits of the Craton were used to train the models; the remaining 30% were considered “undiscovered” and used to validate the models. The output continuous scale prospectivity maps were reclassified into binary prospectivity maps based on the threshold values extracted from area versus prospectivity curves. The weights-of-evidence and logistic regression models, respectively, classify 81% and 86% deposits in high prospectivity zones that occupy about 8% of the total area of the Craton. The superior performance of the logistic regression model is attributed to its capability to accommodate conditional dependence of the input predictor maps.