3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism

Abstract

Mineralization distribution is spatially heterogeneous and jointly controlled by multiple ore-controlling factors. Given this ubiquitous feature, we integrated the attention mechanism of the Convolutional Block Attention Module (CBAM), which contains both channel and spatial attention modules, into a convolutional neural network (CNN) to develop a more robust 3D mineral prospectivity modeling (MPM) approach. A network interpretive algorithm (DeepLIFT) was conducted to the CNN deconstruction to evaluate whether the key ore-controlling information is read. The Sanshandao goldfield in China, with significant fault controls on gold mineralization distribution, was selected as a case study to verify the proposed method. In this case, we generated the 5-channel images by projecting five ore-related fault features to feed into CNN. The optimal prospectivity model of CNN + CBAM was subsequently yielded by a gradient-based optimization of stochastic objective functions and an optimized regularization cross-entropy loss function. By decomposing the constructed model and the vanilla CNN model, we observed the CNN + CBAM has lower feature scores of fault distance and larger scores of fault geometry (e.g., dip and undulation) at Xiling, Haiyu, and Xinli than CNN. These segments occur significant fault strike or dip changes that have influenced mineralization spatial continuity, which is well consistent with the expert knowledge of metallogeny in the Sanshandao goldfield. This means that the CBAM has induced the CNN to focus on key ore-controlling features from complex exploration data, which is likely to improve the model’s prediction ability. The prospectivity results of CNN + CBAM and other comparison shallow machine learning methods (e.g., random forest) revealed that the CNN + CBAM model produced more reliable results, as evidenced by (1) being highly consistent with known mineralization, (2) having the highest AUC values and success rate, and (3) accurately predicting deep voxels that have been explored by drill holes. Thus, the proposed CNN + CBAM method, with a remarkable capacity for emphasizing critical ore-associated features, is recommended for application in deep 3D MPM. Six possible mineralization sites in the Sanshandao field were identified based on the CNN + CBAM prospective data, which may aid future gold exploration.