Mineral potential modelling of orogenic gold systems in the granites-tanami Orogen, Northern Territory, Australia: A multi-technique approach

In this study we adopted fuzzy inference system (FIS), transformed predictor map-based random forest (RF) and feed-forward deep neural network (FF-DNN) approaches to mineral potential modelling (MPM) of the Granites-Tanami Orogen (GTO), Northern Territory, Australia, to (i) predict gold prospectivity, and (ii) compare the performance of these models to those previously generated in the study area. The current study, which utilized 19 predictor maps previously developed by Roshanravan et al. (2020) in the framework of a mineral systems approach, took into consideration the learnings gained and results obtained from previous approaches to modelling the gold potential of the GTO (Roshanravan et al., 2020: continuous fuzzy gamma, geometric average, data-driven index overlay and non-transformed predictor map-based RF; Roshanravan et al., 2021: cuckoo optimization algorithm). Importantly, our study area extending over > 46,000 km2 in the GTO, represents one of the few, if not the only, areas worldwide that has been subjected to eight different modelling techniques, an approach that delivered unique insights that cannot be achieved by utilizing a single technique. For example, our multi-technique approach enabled us to study what effect predictor maps may have on model performance and resulting mineral potential maps. In the case of data-driven MPM with RF, arguably-one of the most popular machine learning techniques, the effect of non-transformed (i.e., original) and transformed (i.e., derivative) predictor maps has never been evaluated simultaneously. To achieve this, we generated a transformed predictor map-based RF potential model with the aim of comparing it to the non-transformed predictor map-based RF model of Roshanravan et al. (2020). The FF-DNN and Mamdani-type FIS approaches to MPM were carried out specifically for the purpose of comparing and further investigating the modelling results. With respect to the Mamdani-type FIS, the concentration-area (C-A) fractal technique served to more objectively determine thresholds of the underlying mathematical functions, a novel approach that aids in reducing bias resulting from human input and expert opinion. The thresholded results demonstrate that the RF potential map generated with transformed predictor maps outperformed all other data-driven and continuous models. Overall, our multi-technique approach to MPM and the comparing and contrasting of a large set of resulting gold potential models, not only offered insights that helped to develop and calibrate new tools and techniques but also delivered what we believe are more robust gold exploration targets. As a result, we strongly recommend to using a multi-technique approach to MPM, which we hope will soon replace the currently accepted and widely utilized single-technique approach