Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model

Abstract

Remote sensing data prove to be an effective resource for constructing a data-driven predictive model of mineral prospectivity. Nonetheless, existing deep learning models predominantly rely on neural networks that necessitate a substantial number of samples, posing a challenge during the early stages of exploration. In order to predict mineral prospectivity using remotely sensed data, this study introduced deep forest (DF), a non-neural network deep learning model. Mainly based on ASTER multispectral imagery supplemented by Sentinel-2 and geological data, gold ore in Hamissana area, NE Sudan was used to test the DF predictive model capability. In addition to four geological-based evidential layers, 20 remote sensing-based evidential layers were generated using remote sensing enhancing techniques, forming the predictor variables of the proposed model. The applicability of the DF was thoroughly examined including its accuracy for delineating prospective areas, sensitivity to amount of training samples, and adjustment of hyperparameters. The results demonstrate that DF model outperformed conventional machine learning models (i.e., support vector machine, artificial neural network, and random forest) with AUC of 0.964 and classification accuracy of 93.3%. Moreover, the sensitivity analysis demonstrated that the DF model can be trained with a limited number (i.e., < 15) of mineral occurrences. Therefore, the DF algorithm has great potential and proves to be a viable solution for data-driven prospectivity mapping, particularly in scenarios with data availability constraints.