BWM-MARCOS: A new hybrid MCDM approach for mineral potential modelling

Abstract

Here we describe the application of a novel hybrid multi-criteria decision-making (MCDM) approach termed “Best-Worst-Method-Measurement of Alternatives and Ranking according to COmpromise Solution” (BWM-MARCOS) to mineral potential modelling (MPM). The newly proposed BWM-MARCOS technique combines two mathematical frameworks in which the BWM approach is utilised to weight decision criteria (i.e., predictor maps) whilst the MARCOS approach is applied to rank alternatives. A BWM-MARCOS model of orogenic gold mineral systems in the well-endowed Granites-Tanami Orogen (GTO) in Australia's Northern Territory was generated utilising the same study area and set of competent spatial proxies as previously developed and described by Roshanravan et al. (2020), facilitating the benchmarking of the new results against those obtained from previous models. We found the BWM-MARCOS approach to MPM performed better than any of the knowledge-driven (i.e., fuzzy inference system), continuous (i.e., data-driven index overlay, geometric average and fuzzy gamma) and data-driven (i.e., feed-forward deep neural network and ‘original’ random forest) mineral prospectivity models previously developed for the GTO by Roshanravan et al. (2020, 2023b). To further constrain the BWM-MARCOS outputs, the modeled gold potential zones were delimited utilising a concentration-area fractal approach. The areas covered by the prioritised 1st order (top priority) and 2nd order (high priority) targets represent significant reductions of the search space (i.e., >303 times or two orders of magnitude, and >93 times or one order of magnitude, respectively). Any order of magnitude, or greater, reduction of the search space, as achieved in this study, can be considered a hallmark of a well-performing, practically useful targeting approach.