Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 8
Articles | Volume 19, issue 8
https://doi.org/10.5194/gmd-19-3455-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-3455-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 8
Development and technical paper
 | 
27 Apr 2026
Development and technical paper |  | 27 Apr 2026

Curlew 1.0: Spatio-temporal implicit geological modelling with neural fields in python

Akshay V. Kamath, Samuel T. Thiele, Marie Moulard, Lachlan Grose, Raimon Tolosana-Delgado, Michael J. Hillier, Florian Wellmann, and Richard Gloaguen

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Short summary
We present Curlew, an open-source Python tool for constructing 3D geological models using machine learning. It integrates diverse spatial data and structural observations into a flexible, event-based framework. Curlew captures complex features like folds and faults, handles uncertainty, and supports learning from sparse or unlabelled data. We demonstrate its capabilities on synthetic and real-world examples.
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