Articles | Volume 19, issue 7
https://doi.org/10.5194/gmd-19-2593-2026
https://doi.org/10.5194/gmd-19-2593-2026
Model description paper
 | 
07 Apr 2026
Model description paper |  | 07 Apr 2026

DEEP-SEAM: an explainable semi-supervised deep learning framework for mineral prospectivity mapping

Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, and R. Dietmar Müller

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Cited articles

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Alonso, E., Sherman, A. M., Wallington, T. J., Everson, M. P., Field, F. R., Roth, R., and Kirchain, R. E.: Evaluating Rare Earth Element Availability: A Case with Revolutionary Demand from Clean Technologies, Environ. Sci. Technol., 46, 3406–3414, https://doi.org/10.1021/es203518d, 2012. 
Alper, O. C., Doğan, H., and Öztürk, H.: Gear pitting fault detection: Leveraging anomaly detection methods, in: IEEE 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), 1–5, https://doi.org/10.1109/ELECO60389.2023.10416063, 2023. 
Austin, J. and Foss, C.: Rich, attractive and extremely dense: A geophysical review of Australian IOCGs, ASEG Extend. Abstr., 2012, 1–4, https://doi.org/10.1071/ASEG2012ab278, 2012. 
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., and Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview, Bioinformatics, 16, 412–424, https://doi.org/10.1093/bioinformatics/16.5.412, 2000. 
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Short summary
By combining multi-source data with advanced processing techniques, our deep learning model effectively identifies mineralisation patterns despite extremely limited deposit samples, analyses data and validates the geological relevance of its decisions through explainability analysis, providing a universally reliable solution for artificial intelligence-assisted mineral prospectivity mapping.
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