Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

The excellent performance of convolutional neural network (CNN) and its variants in image classification makes it a potential perfect candidate for dealing with multi-geoinformation involving abundant spatial information. In this paper, we tested, for data-driven mineral prospectivity mapping, the efficacy of using unsupervised convolutional auto-encoder network (CAE) to support CNN modeling for synthesis of multi-geoinformation. First, two simple unsupervised CAE networks were constructed to distinguish patches of tif image (i.e., nine predictive evidence maps forming a tif-format image) with nine channels that have high reconstructed errors, which represent prospective areas (i.e., mineralized). Then, the patches of tif image with the lowest reconstructed errors were regarded as background (or non-prospective areas). We varied the CAE network architecture and training epochs and combinations of evidence maps for trials to obtain reliable results. Then, the AUC, or area under the receiver operating characteristic curve, was used to demonstrate empirically that high reconstructed errors are representative of spatial signatures of prospective areas. The proposed coherent spatial signatures, namely patches of a tif image with the highest reconstructed errors and the lowest reconstructed errors representing prospective and non-prospective areas, respectively, were used in the subsequent CNN modeling. The results of CNN modeling using training data derived from CAE exhibited strong spatial correlation with known Au deposits in the study area. The training loss and accuracy of the CNN modeling together with resulting favorability map that were comparable with results from previous study proved the plausibility of the proposed methodology, and therefore, the practice of extracting coherent spatial signatures of prospective and non-prospective areas in unsupervised manner using CAE network and then using these coherent spatial signatures in supervised learning with CNN is a new potential approach for mineral prospectivity mapping.