Articles | Volume 15, issue 1
https://doi.org/10.5194/se-15-63-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-15-63-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Method article
| 
02 Feb 2024
Method article |
| 02 Feb 2024
| 02 Feb 2024
Integration of automatic implicit geological modelling in deterministic geophysical inversion
Jérémie Giraud
CORRESPONDING AUTHOR
GeoRessources, Université de Lorraine, CNRS, 54000 Nancy, France
Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia
Guillaume Caumon
GeoRessources, Université de Lorraine, CNRS, 54000 Nancy, France
Lachlan Grose
School of Earth Atmosphere and Environment, Monash University, 3800 Melbourne, VIC, Australia
Vitaliy Ogarko
Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia
Mineral Exploration Cooperative Research Centre, University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia
Paul Cupillard
GeoRessources, Université de Lorraine, CNRS, 54000 Nancy, France
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Cited
16 citations as recorded by crossref.
- Distance-regularized level set inversion of magnetic data J. Liu et al. https://doi.org/10.1190/geo2023-0329.1
- A Deep Learning-Based Adaptive Regularization Framework for Electromagnetic Inversion Z. Li et al. https://doi.org/10.1109/TGRS.2026.3705795
- Review of Advancements in Geometry-Based Inversion of Geophysical Data Sets S. Vatankhah et al. https://doi.org/10.1007/s10712-025-09902-x
- Joint inversion of seismic and geoelectrical data targeting geological faults M. Arboleda-Zapata et al. https://doi.org/10.1093/gji/ggag361
- Gravity data inversion of the Pyrenees range using Taguchi sensitivity analysis and ADMM bound constraints based on seismic data R. Martin et al. https://doi.org/10.1093/gji/ggae410
- GeoSAE: A 3D Stratigraphic Modeling Method Driven by Geological Constraint Y. Yang et al. https://doi.org/10.3390/app15031185
- Physics-guided deep learning for subsurface characterization: From resource exploration to engineering site investigation—A critical review H. Wen et al. https://doi.org/10.1016/j.jappgeo.2026.106515
- Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees J. Giraud et al. https://doi.org/10.1093/gji/ggae192
- Pseudo trans-dimensional 3-D geometrical inversion: a proof of concept using gravity data J. Giraud et al. https://doi.org/10.1093/gji/ggaf501
- LFD (v1.0): latent-compression-free generative diffusion with geological priors and geophysical regularization for implicit structural modeling Z. Guo et al. https://doi.org/10.5194/gmd-19-7961-2026
- VolSur: Efficient surface reconstruction for geological volumes with distinct constraints D. Zhong et al. https://doi.org/10.1016/j.cageo.2026.106263
- Checking the consistency of 3D geological models M. Parquer et al. https://doi.org/10.5194/gmd-18-71-2025
- A novel methodology for geological modeling using a multilayer perceptron with supervised learning based on an implicit approach H. Hernández et al. https://doi.org/10.22201/igc.20072902e.2026.2.1879
- Three-dimensional geological modeling based on dual-task stratigraphy-aware attention networks (Geo-SAN v1.0) Z. Fang et al. https://doi.org/10.5194/gmd-19-8407-2026
- Estimating the geometry of magnetization distributions within a geological sample from magnetic microscopy images L. Sinimbu et al. https://doi.org/10.1016/j.measurement.2025.118130
- Structure-based geophysical inversion using implicit geological models A. Balza-Morales et al. https://doi.org/10.1093/gji/ggaf445
16 citations as recorded by crossref.
- Distance-regularized level set inversion of magnetic data J. Liu et al. https://doi.org/10.1190/geo2023-0329.1
- A Deep Learning-Based Adaptive Regularization Framework for Electromagnetic Inversion Z. Li et al. https://doi.org/10.1109/TGRS.2026.3705795
- Review of Advancements in Geometry-Based Inversion of Geophysical Data Sets S. Vatankhah et al. https://doi.org/10.1007/s10712-025-09902-x
- Joint inversion of seismic and geoelectrical data targeting geological faults M. Arboleda-Zapata et al. https://doi.org/10.1093/gji/ggag361
- Gravity data inversion of the Pyrenees range using Taguchi sensitivity analysis and ADMM bound constraints based on seismic data R. Martin et al. https://doi.org/10.1093/gji/ggae410
- GeoSAE: A 3D Stratigraphic Modeling Method Driven by Geological Constraint Y. Yang et al. https://doi.org/10.3390/app15031185
- Physics-guided deep learning for subsurface characterization: From resource exploration to engineering site investigation—A critical review H. Wen et al. https://doi.org/10.1016/j.jappgeo.2026.106515
- Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees J. Giraud et al. https://doi.org/10.1093/gji/ggae192
- Pseudo trans-dimensional 3-D geometrical inversion: a proof of concept using gravity data J. Giraud et al. https://doi.org/10.1093/gji/ggaf501
- LFD (v1.0): latent-compression-free generative diffusion with geological priors and geophysical regularization for implicit structural modeling Z. Guo et al. https://doi.org/10.5194/gmd-19-7961-2026
- VolSur: Efficient surface reconstruction for geological volumes with distinct constraints D. Zhong et al. https://doi.org/10.1016/j.cageo.2026.106263
- Checking the consistency of 3D geological models M. Parquer et al. https://doi.org/10.5194/gmd-18-71-2025
- A novel methodology for geological modeling using a multilayer perceptron with supervised learning based on an implicit approach H. Hernández et al. https://doi.org/10.22201/igc.20072902e.2026.2.1879
- Three-dimensional geological modeling based on dual-task stratigraphy-aware attention networks (Geo-SAN v1.0) Z. Fang et al. https://doi.org/10.5194/gmd-19-8407-2026
- Estimating the geometry of magnetization distributions within a geological sample from magnetic microscopy images L. Sinimbu et al. https://doi.org/10.1016/j.measurement.2025.118130
- Structure-based geophysical inversion using implicit geological models A. Balza-Morales et al. https://doi.org/10.1093/gji/ggaf445
Saved (final revised paper)
Latest update: 24 Sep 2026
Short summary
We present and test an algorithm that integrates geological modelling into deterministic geophysical inversion. This is motivated by the need to model the Earth using all available data and to reconcile the different types of measurements. We introduce the methodology and test our algorithm using two idealised scenarios. Results suggest that the method we propose is effectively capable of improving the models recovered by geophysical inversion and may be applied in real-world scenarios.
We present and test an algorithm that integrates geological modelling into deterministic...
