Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-8755-2026
© 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-19-8755-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Uncertainty quantification of deep learning algorithms for mineral prospectivity mapping
Ziye Wang
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
Renguang Zuo
CORRESPONDING AUTHOR
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
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Jian Wang, Renguang Zuo, Dazheng Huang, and Mingang Liu
EGUsphere, https://doi.org/10.5194/egusphere-2026-1126, https://doi.org/10.5194/egusphere-2026-1126, 2026
Short summary
Short summary
Geostatistical simulation is widely used to quantify uncertainty, but it can be demanding for large or complex datasets. We explored a data-driven approach to learn spatial patterns for simulation. Through several experiments, we show that the model can reproduce key statistical properties while being less sensitive to non-normal data and changing spatial conditions. The method provides a flexible complement to traditional simulation, especially when many nearby data points are used.
Jian Wang, Renguang Zuo, and Qinghai Liu
Solid Earth, 15, 731–746, https://doi.org/10.5194/se-15-731-2024, https://doi.org/10.5194/se-15-731-2024, 2024
Short summary
Short summary
This study improves geochemical mapping by addressing the uncertainty in defining element associations. It clusters the study area by element similarity, recognizes elemental associations for each cluster, and then detects anomalies indicating underlying geological processes. This method is applied to a region in China, confirming its effectiveness and consistency with the geology. This study can enhance geochemical mapping for mineral exploration and improve geological-process understanding.
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Short summary
This study proposes a comprehensive uncertainty quantification framework that jointly evaluates data, model, and prediction uncertainties in deep learning-based mineral prospectivity mapping. By modelling and visualizing both data and model uncertainties, the framework transforms deep learning-based mineral prospectivity mapping from deterministic prediction to probabilistic decision-making, thereby enabling more reliable and trustworthy mineral exploration.
This study proposes a comprehensive uncertainty quantification framework that jointly evaluates...