Abstract
Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical survey data. Yet purely data-driven DL algorithms often lack logical explanations and geological consistency, occasionally clashing with known geological insights and complicating model interpretation. A deep understanding of the geological processes forming the target mineral deposit is vital for accurate anomaly detection. Here, we introduce an adversarial autoencoder (AAE) network that integrates prior geological knowledge to identify geochemical anomalies linked to tungsten mineralization in southern Jiangxi Province, China. Considering the geochemical patterns linked to tungsten mineralization, Yanshanian granites and faults were strategically chosen as ore-controlling factors. The methodology employed multifractal singularity analysis to quantitatively measure the correlations between these ore-controlling factors and known tungsten deposits, aiming to establish an ore-forming regularity. This regularity serves as a priori distribution to control the encoder network's latent vector, refining the model's output. A comparison of detected geochemical anomalies under different constraints (AAE, Granite_AAE, Fault_AAE, and Fault_Granite_AAE) revealed that AAE models incorporating prior geological information consistently outperformed unconstrained models in terms of anomaly detection. Integrating geological expertise with DL, our study overcomes the challenges of models relying purely on data or theory, offering a promising approach to geochemical exploration.
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Acknowledgements
We are grateful to two reviewers for comments and suggestions which helped us improve this study. This study was supported by the Natural Science Foundation of Hubei Province (China) (2023AFA001), and the National Natural Science Foundation of China (42172326, 42321001).
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Zhang, C., Zuo, R. Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability. Math Geosci 56, 1233–1254 (2024). https://doi.org/10.1007/s11004-023-10133-2
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DOI: https://doi.org/10.1007/s11004-023-10133-2