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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
29 Nov 2023
Development and technical paper |
| 29 Nov 2023
| 29 Nov 2023
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
Michael Hillier
CORRESPONDING AUTHOR
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Florian Wellmann
Computational Geoscience and Reservoir Engineering (CGRE), RWTH Aachen University, Mathieustr. 30, 52074 Aachen, Germany
Eric A. de Kemp
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Boyan Brodaric
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Ernst Schetselaar
Geological Survey of Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON K1A 0E8, Canada
Karine Bédard
Geological Survey of Canada, Natural Resources Canada, 490 rue de la Couronne, Quebec City, QC G1K 9A9, Canada
Viewed
Total article views: 6,394 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Mar 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,768 | 1,499 | 127 | 6,394 | 144 | 226 |
- HTML: 4,768
- PDF: 1,499
- XML: 127
- Total: 6,394
- BibTeX: 144
- EndNote: 226
Total article views: 4,930 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 Nov 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,873 | 960 | 97 | 4,930 | 129 | 211 |
- HTML: 3,873
- PDF: 960
- XML: 97
- Total: 4,930
- BibTeX: 129
- EndNote: 211
Total article views: 1,464 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Mar 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 895 | 539 | 30 | 1,464 | 15 | 15 |
- HTML: 895
- PDF: 539
- XML: 30
- Total: 1,464
- BibTeX: 15
- EndNote: 15
Viewed (geographical distribution)
Total article views: 6,394 (including HTML, PDF, and XML)
Thereof 6,193 with geography defined
and 201 with unknown origin.
Total article views: 4,930 (including HTML, PDF, and XML)
Thereof 4,756 with geography defined
and 174 with unknown origin.
Total article views: 1,464 (including HTML, PDF, and XML)
Thereof 1,437 with geography defined
and 27 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
20 citations as recorded by crossref.
- Maximising the value of hyperspectral drill core scanning through real-time processing and analysis S. Thiele et al. https://doi.org/10.3389/feart.2024.1433662
- Variational prior replacement in Bayesian inference and inversion X. Zhao & A. Curtis https://doi.org/10.1093/gji/ggae334
- GraphFlow v1.0: approximating groundwater contaminant transport with graph-based methods – an application to fault scenario selection L. Moracchini et al. https://doi.org/10.5194/gmd-18-7147-2025
- GeoSAE: A 3D Stratigraphic Modeling Method Driven by Geological Constraint Y. Yang et al. https://doi.org/10.3390/app15031185
- Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics S. Garzón et al. https://doi.org/10.1016/j.cageo.2025.106043
- Curlew 1.0: Spatio-temporal implicit geological modelling with neural fields in python A. Kamath et al. https://doi.org/10.5194/gmd-19-3455-2026
- 3D Modeling of Tectonostratigraphic, Petrophysical, and Uranium Mineralization Properties for Sandstone-Type Uranium Reserve Assessment in the Shawan Formation, Chepaizi Area, Junggar Basin T. He et al. https://doi.org/10.1007/s11053-025-10631-6
- Variability of geological interpretations of a mineral deposit: Quantitative study and implications P. Marchal et al. https://doi.org/10.1016/j.oregeorev.2025.107010
- Implicit 3D Orebody Boundary Modeling Based on Adaptive Finite Difference Method Z. Wang et al. https://doi.org/10.3390/min16050541
- GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds D. Oakley et al. https://doi.org/10.5194/gmd-18-939-2025
- CurvRBF: Mean Curvature-Controllable Radial Basis Functions for Implicit Geological Modeling Y. Chen et al. https://doi.org/10.1007/s11004-025-10226-0
- Tensorweave 1.0: interpolating geophysical tensor fields with spatial neural networks A. Kamath et al. https://doi.org/10.5194/gmd-18-7951-2025
- GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data J. Guo et al. https://doi.org/10.5194/gmd-17-957-2024
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann https://doi.org/10.1016/j.cageo.2025.105911
- A weighted ML–Kriging 3D geological model integrated with deep iterative optimization H. Liu et al. https://doi.org/10.1016/j.gsme.2026.01.004
- An implicit storage method for 3D geological models supporting multiscale modelling and cross-sectional analysis Z. Liu et al. https://doi.org/10.1016/j.cageo.2026.106204
- Uncertainty quantification using Hamiltonian Monte Carlo for structural geological modelling with implicit neural representations (INR) K. Gao et al. https://doi.org/10.1016/j.cageo.2026.106123
- Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis X. Cao et al. https://doi.org/10.3390/min14070686
- Mapping the Underground: Geotechnical Physical Properties Insights from Bengkulu City L. Mase et al. https://doi.org/10.1007/s40515-025-00765-8
- Multiple-point geostatistical modeling for fault-controlled tight sandstone reservoirs based on probability fusion of permanence of ratios: a tight sandstone oil reservoir in the southern margin of the ordos basin H. Zhang et al. https://doi.org/10.3389/feart.2025.1552058
20 citations as recorded by crossref.
- Maximising the value of hyperspectral drill core scanning through real-time processing and analysis S. Thiele et al. https://doi.org/10.3389/feart.2024.1433662
- Variational prior replacement in Bayesian inference and inversion X. Zhao & A. Curtis https://doi.org/10.1093/gji/ggae334
- GraphFlow v1.0: approximating groundwater contaminant transport with graph-based methods – an application to fault scenario selection L. Moracchini et al. https://doi.org/10.5194/gmd-18-7147-2025
- GeoSAE: A 3D Stratigraphic Modeling Method Driven by Geological Constraint Y. Yang et al. https://doi.org/10.3390/app15031185
- Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics S. Garzón et al. https://doi.org/10.1016/j.cageo.2025.106043
- Curlew 1.0: Spatio-temporal implicit geological modelling with neural fields in python A. Kamath et al. https://doi.org/10.5194/gmd-19-3455-2026
- 3D Modeling of Tectonostratigraphic, Petrophysical, and Uranium Mineralization Properties for Sandstone-Type Uranium Reserve Assessment in the Shawan Formation, Chepaizi Area, Junggar Basin T. He et al. https://doi.org/10.1007/s11053-025-10631-6
- Variability of geological interpretations of a mineral deposit: Quantitative study and implications P. Marchal et al. https://doi.org/10.1016/j.oregeorev.2025.107010
- Implicit 3D Orebody Boundary Modeling Based on Adaptive Finite Difference Method Z. Wang et al. https://doi.org/10.3390/min16050541
- GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds D. Oakley et al. https://doi.org/10.5194/gmd-18-939-2025
- CurvRBF: Mean Curvature-Controllable Radial Basis Functions for Implicit Geological Modeling Y. Chen et al. https://doi.org/10.1007/s11004-025-10226-0
- Tensorweave 1.0: interpolating geophysical tensor fields with spatial neural networks A. Kamath et al. https://doi.org/10.5194/gmd-18-7951-2025
- GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data J. Guo et al. https://doi.org/10.5194/gmd-17-957-2024
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann https://doi.org/10.1016/j.cageo.2025.105911
- A weighted ML–Kriging 3D geological model integrated with deep iterative optimization H. Liu et al. https://doi.org/10.1016/j.gsme.2026.01.004
- An implicit storage method for 3D geological models supporting multiscale modelling and cross-sectional analysis Z. Liu et al. https://doi.org/10.1016/j.cageo.2026.106204
- Uncertainty quantification using Hamiltonian Monte Carlo for structural geological modelling with implicit neural representations (INR) K. Gao et al. https://doi.org/10.1016/j.cageo.2026.106123
- Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis X. Cao et al. https://doi.org/10.3390/min14070686
- Mapping the Underground: Geotechnical Physical Properties Insights from Bengkulu City L. Mase et al. https://doi.org/10.1007/s40515-025-00765-8
- Multiple-point geostatistical modeling for fault-controlled tight sandstone reservoirs based on probability fusion of permanence of ratios: a tight sandstone oil reservoir in the southern margin of the ordos basin H. Zhang et al. https://doi.org/10.3389/feart.2025.1552058
Saved (final revised paper)
Latest update: 13 Jun 2026
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.
Neural networks can be used effectively to model three-dimensional geological structures from...
