An interpretable attention branch convolutional neural network for identifying geochemical anomalies related to mineralization

Abstract

Geochemical anomalies play a crucial role in geochemical prospecting as they directly indicate the existence of mineralization. Deep learning algorithms (DLAs) have received increasing attention in the domain of multivariate geochemical anomaly recognition due to their extraordinary ability to extract complex and nonlinear geochemical patterns. However, the black-box nature inherent in DLAs typically prevents one from understanding the potential operational mechanisms behind the recognition procedure, resulting in lack of confidence in the identification results. In this study, visualization techniques and attention mechanisms were integrated into a convolutional neural network (CNN) as an attention branch in order to construct an interpretable attention branch convolutional neural network (ABCNN) model for geochemical anomaly identification. Specifically, the class activation mapping was applied to visualize the spatial features extracted by the model and to identify important regions during model inference. The attention mechanism was further incorporated into a CNN to generate attention maps for enhancing spatial features associated with mineralization and interpreting the recognition results based on attention weights. A case study for identifying multivariate geochemical anomalies linked to Au polymetallic mineralization in the western Henan Province of China was presented to illustrate and validate the feasibility of the ABCNN model. The geochemical anomalies delineated by the ABCNN model coincide well with known gold deposits and ore-controlling geological features. The comparative studies between the ABCNN and a traditional CNN further manifested that the incorporation of an attention mechanism can both effectively improve the model interpretability and performance.