Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms

Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey data for mineral exploration. However, it is difficult to understand their working mechanisms and decision-making behaviors, which may lead to unreliable results. The construction of a reliable and interpretable DLA has become a focus in data-driven geoscience discovery. This study utilized a SHapley Additive exPlanations (SHAP) framework, a popular post-hoc interpretability analysis method, incorporated with the variational autoencoder (VAE) to explore the contribution of geochemical elements for multivariate geochemical anomaly recognition. The sorting of element importance obtained by SHAP tool can provide a novel view for selecting a suitable elemental association related to mineralization. Based on the metallogenic model in the southeastern Hubei Province of China, a metallogenic-factor-based VAE model was constructed using an ad-hoc interpretable modeling technique. The interpretability of the model in identifying the abnormal distribution of the element associations can be improved by constructing a hidden layer and loss function containing metallogenic regularity and key metallogenic factors. The highly anomalous areas identified by the metallogenic-factor VAE model not only contain most of the known Au deposits, but also can reasonably identify the abnormal elemental associations related to ore-forming processes under the guidance of the metallogenic regularity. According to the output visualization of the new hidden layer, and the results of receiver operating characteristic curve and success-rate curve, the metallogenic-factor VAE model exhibits satisfied interpretability and performance. The geochemical anomalies identified in this study provide critical clues for future mineral exploration.

Automated summary

This study utilized the SHAP framework combined with VAE to construct a reliable and interpretable DML model. It selected suitable elemental associations related to mineralization by sorting the importance of elements. The metallogenic-factor VAE model constructed based on the metallogenic model in Hubei Province exhibited satisfactory interpretability and performance, providing critical clues for future mineral exploration.