Articles | Volume 19, issue 7
https://doi.org/10.5194/gmd-19-2593-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2593-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DEEP-SEAM: an explainable semi-supervised deep learning framework for mineral prospectivity mapping
Zijing Luo
CORRESPONDING AUTHOR
School of Resources and Environment, Henan Polytechnic University, Jiaozuo, 454003, Henan, China
EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia
Ehsan Farahbakhsh
EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia
Stephen Hore
Geological Survey of South Australia, Adelaide, Australia
R. Dietmar Müller
EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia
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We have built a community model for the evolution of the Earth's plate–mantle system. Created with open-source software and an open-access plate model, it covers the last billion years, including the formation, breakup, and dispersal of two supercontinents, as well as the creation and destruction of numerous ocean basins. The model allows us to
seeinto the Earth in 4D and helps us unravel the connections between surface tectonics and the
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Eline Le Breton, Sascha Brune, Kamil Ustaszewski, Sabin Zahirovic, Maria Seton, and R. Dietmar Müller
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The former Piemont–Liguria Ocean, which separated Europe from Africa–Adria in the Jurassic, opened as an arm of the central Atlantic. Using plate reconstructions and geodynamic modeling, we show that the ocean reached only 250 km width between Europe and Adria. Moreover, at least 65 % of the lithosphere subducted into the mantle and/or incorporated into the Alps during convergence in Cretaceous and Cenozoic times comprised highly thinned continental crust, while only 35 % was truly oceanic.
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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.
By combining multi-source data with advanced processing techniques, our deep learning model...