Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines

Abstract

An application of logistic regression to mapping of gold potential in the Baguio district of the Philippines is described. Categorical map data such as lithologic units and proximity classes of curvi-linear features, based on spatial association analyses, are quantified systematically and used as independent variables in logistic regression to predict the probability for presence or absence of gold mineralization. Regression experiments to compare between using all independent variables that are associated spatially with the response variable and using only statistically significant independent variables are performed. The results of the regression experiments are similar; however, the use of all independent variables produces slightly optimistic results but better prediction rates for the known gold deposits in the test district. At least 68% of the ‘model’ large-scale gold deposits and at least 76% of the ‘validation’ small-scale gold deposits were predicted correctly. The predicted geologically favorable zones are also similar to delineated geochemically anomalous zones. The technique presented using logistic regression as a data integration tool is effective for geologically constrained technique of mapping mineral potential.