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Articles | Volume 16, issue 4/5
Articles | Volume 16, issue 4/5
https://doi.org/10.5194/se-16-351-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-16-351-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 4/5
Research article
 | 
15 May 2025
Research article |  | 15 May 2025

Multiphysics property prediction from hyperspectral drill core data

Akshay V. Kamath, Samuel T. Thiele, Moritz Kirsch, and Richard Gloaguen

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Short summary
We developed a deep learning model that uses hyperspectral imaging data to predict key physical rock properties, specifically density, slowness, and gamma-ray values. Our model successfully learned to translate hyperspectral information into predicted physical properties. Tests on independent data gave accurate results, demonstrating the potential of hyperspectral data for mapping physical rock properties.
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