Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines

Abstract

Binary predictor patterns of geological features are integrated based on a probabilistic approach known as weights of evidence modeling to predict gold potential. In weights of evidence modeling, the log _e of the posterior odds of a mineral occurrence in a unit cell is obtained by adding a weight, W ⁺ or W ⁻ for presence of absence of a binary predictor pattern, to the log _e of the prior probability. The weights are calculated as log _e ratios of conditional probabilities. The contrast, C = W ⁺ − W ⁻, provides a measure of the spatial association between the occurrences and the binary predictor patterns. Addition of weights of the input binary predictor patterns results in an integrated map of posterior probabilities representing gold potential. Combining the input binary predictor patterns assumes that they are conditionally independent from one another with respect to occurrences.