Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 18
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.
https://doi.org/10.5194/gmd-19-8755-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 18
Development and technical paper
 | 
18 Sep 2026
Development and technical paper |  | 18 Sep 2026

Uncertainty quantification of deep learning algorithms for mineral prospectivity mapping

Ziye Wang and Renguang Zuo

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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.
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