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

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

doi:10.5194/gmd-19-3455-2026

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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Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 3,362 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
2,841	370	151	3,362	109	124

HTML: 2,841
PDF: 370
XML: 151
Total: 3,362
BibTeX: 109
EndNote: 124

Views and downloads (calculated since 13 Nov 2025)

Month	HTML	PDF	XML	Total
Nov 2025	148	0	148
Dec 2025	106	6	112
Jan 2026	156	8	164
Feb 2026	172	4	176
Mar 2026	270	6	276
Apr 2026	498	85	8	591
May 2026	657	109	24	790
Jun 2026	89	19	5	113
Jul 2026	125	27	5	157
Aug 2026	128	22	7	157
Sep 2026	380	86	68	534
Oct 2026	112	22	10	144

Cumulative views and downloads (calculated since 13 Nov 2025)

Month	HTML	PDF	XML	Total
Nov 2025	148	0	148
Dec 2025	106	6	112
Jan 2026	156	8	164
Feb 2026	172	4	176
Mar 2026	270	6	276
Apr 2026	498	85	8	591
May 2026	657	109	24	790
Jun 2026	89	19	5	113
Jul 2026	125	27	5	157
Aug 2026	128	22	7	157
Sep 2026	380	86	68	534
Oct 2026	112	22	10	144

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 3,362 (including HTML, PDF, and XML) Thereof 3,280 with geography defined and 82 with unknown origin.

Country	#	Views	%

1

1

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Latest update: 11 Oct 2026

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.