Geological-knowledge-guided graph self-supervised pretraining framework for identifying mineralization-related geochemical anomalies

Abstract

The identification of geochemical anomalies related to mineralization is crucial for predicting the presence of mineral resources. Graph neural networks are essential tools in identifying geochemical anomalies associated with mineralization owing to their ability to process spatially correlated data. However, the performance and generalizability of supervised learning models are often constrained by the limited availability of labeled data. As such, graph self-supervised learning (SSL) has received considerable attention because of its ability to strengthen representation learning by capturing the intrinsic structure and distribution of data, even in scenarios in which labeled data are scarce. The existing SSL methods often fail to effectively incorporate domain-specific knowledge during the learning process, restricting both the generalization and geological interpretability of SSL models. As such, a geological-knowledge-guided graph with self-supervised pretraining (GKGP) framework was proposed in this study. The GKGP framework substantially enhanced the SSL model's ability to capture important spatial relationships and geochemical features by embedding the power-law relationship between the spatial density of mineral deposits and their distance to ore-controlling factors into the pretraining phase of the SSL, which is combined with a transformer-based multi-head attention mechanism. A case study was conducted in the Suizao District, Hubei Province, China, to demonstrate that the proposed GKGP framework can be used to identify mineralization-related geochemical anomalies, even under high-intensity data perturbations, maintaining robust predictive performance. Additionally, the integration of prior geological knowledge notably increases the accuracy and interpretability of the SSL model, ensuring robustness under complex conditions. The proposed pretraining strategy provides reliable geochemical insights to guide future mineral exploration in the study area.