Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 23
Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-6987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-6987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 23
Development and technical paper
 | 
29 Nov 2023
Development and technical paper |  | 29 Nov 2023

GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling

Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard

Viewed

Total article views: 4,331 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,353 886 92 4,331 101 156
  • HTML: 3,353
  • PDF: 886
  • XML: 92
  • Total: 4,331
  • BibTeX: 101
  • EndNote: 156
Views and downloads (calculated since 08 Mar 2023)
Cumulative views and downloads (calculated since 08 Mar 2023)

Viewed (geographical distribution)

Total article views: 4,331 (including HTML, PDF, and XML) Thereof 4,123 with geography defined and 208 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Nov 2025
Download
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Read more
Share
Special issue
The Loop 3D stochastic geological modelling platform – development...