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A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping

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Abstract

Deep learning algorithms (DLAs) are becoming popular tools for mineral prospectivity mapping. However, purely data-driven DLAs frequently ignore expert and domain knowledge, imposing difficulty in interpretability from a geological perspective. The efficient integration of geological knowledge into DLAs remains challenging in geosciences. In this study, a geologically constrained variational autoencoder (VAE) was proposed to map prospectivity for gold mineralization in the Baguio District of the Philippines. A spatial nonlinear correlation between an ore-forming controlling feature and locations of mineral deposits was built as part of the loss function for constructing a geologically constrained VAE. A comparative study of a geologically constrained and a traditional VAE demonstrated that the former can enhance the probabilities in areas with high potential for locating mineralization and increase the interpretability of the obtained results.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant No. 42172326). We thank six reviewers’ comments and suggestions which helped us improve this study.

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  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Renguang Zuo, Zijing Luo, Yihui Xiong & Bojun Yin

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  1. Renguang Zuo
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Correspondence to Renguang Zuo.

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Zuo, R., Luo, Z., Xiong, Y. et al. A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping. Nat Resour Res 31, 1121–1133 (2022). https://doi.org/10.1007/s11053-022-10050-x

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