Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India

Abstract

Approaches to mineral potential mapping based on weights of evidence generally use binary maps, whereas, real-world geospatial data are mostly multi-class in nature. The consequent reclassification of multi-class maps into binary maps is a simplification that might result in a loss of information. This paper describes results of using multi-class evidential maps in an extended weights-of-evidence model vis-a-vis results of using binary evidential maps in a simple-weights-of-evidence model. The study area in the south-central part of Aravalli province (western India) hosts a number of SEDEX-type base metal deposits in Proterozoic supracrustal rocks. Recognition criteria for base metal deposits were represented as both multi-class and binary evidential maps. The known mineral deposits were divided into two subsets, viz., the training and the validation subsets. The training subset was used to calculate, for the evidential maps, the weights, contrasts, and posterior probabilities and their variances. The distributions of expected frequencies of base metal deposits estimated from the posterior probabilities and the observed frequencies were compared using standard goodness-of-fit tests to verify conditional independence of the input evidential maps. The posterior probabilities from both the models were mapped and interpreted to classify the study area into zones favorable, permissive, and non-permissive for base metal deposit occurrence. As compared to the simple weights-of-evidence model, the extended weights-of-evidence model results in more robust and finely differentiated posterior probabilities in favorable and permissive zones and has a better prediction rate. The results also reveal that the statistical properties of the weights of evidence, the contrasts, and the posterior probabilities are not significantly degenerated by using multi-class evidential maps in weights-of-evidence modelling.