Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7951-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7951-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Development and technical paper
 | 
28 Oct 2025
Development and technical paper |  | 28 Oct 2025

Tensorweave 1.0: interpolating geophysical tensor fields with spatial neural networks

Akshay V. Kamath, Samuel T. Thiele, Hernan Ugalde, Bill Morris, Raimon Tolosana-Delgado, Moritz Kirsch, and Richard Gloaguen

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Short summary
We present a new machine learning approach to reconstruct gravity and magnetic tensor data from sparse airborne surveys. By treating the data as derivatives of a hidden potential field and enforcing physical laws, our method improves accuracy and captures geological features more clearly. This enables better subsurface imaging in regions where traditional interpolation methods fall short.
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