Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3899-2021
© Author(s) 2021. 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-14-3899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Jun 2021
Development and technical paper |
| 28 Jun 2021
| 28 Jun 2021
Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1
Alexander Schaaf
Geology and Petroleum Geology, School of Geosciences, University of Aberdeen, Aberdeen, AB24 3UE, UK
Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen, Germany
Miguel de la Varga
Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen, Germany
Florian Wellmann
CORRESPONDING AUTHOR
Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen, Germany
Clare E. Bond
Geology and Petroleum Geology, School of Geosciences, University of Aberdeen, Aberdeen, AB24 3UE, UK
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Cited
17 citations as recorded by crossref.
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann https://doi.org/10.1016/j.cageo.2025.105911
- Checking the consistency of 3D geological models M. Parquer et al. https://doi.org/10.5194/gmd-18-71-2025
- Application and Validation of PSO-k-Means Clustering for Small-Scale Fault Identification in Coal Mines: A Case Study of the Huainan Coalfield B. Wang et al. https://doi.org/10.1007/s00603-025-04991-x
- Geological Modelling of Urban Environments Under Data Uncertainty C. Ntigkakis et al. https://doi.org/10.3390/geosciences15110423
- Semantic rule-guided three-dimensional reservoir modeling method using an improved multiple-point geostatistics simulation Q. Chen et al. https://doi.org/10.3389/feart.2026.1766398
- Developing a 3D hydrostratigraphical model of the emerged part of the Pelotas Basin along the northern coast of Rio Grande do Sul state, Brazil L. Marquetto et al. https://doi.org/10.1007/s12665-024-11609-y
- Using 3D models to test geological hypotheses for sill complex geometries: Application to Jurassic dolerite intrusions, Tasmania F. Alvarado-Neves et al. https://doi.org/10.1016/j.jsg.2025.105420
- Informed Local Smoothing in 3D Implicit Geological Modeling J. von Harten et al. https://doi.org/10.3390/min11111281
- Incorporating geological structure into sensitivity analysis of subsurface contaminant transport L. Bigler et al. https://doi.org/10.1016/j.advwatres.2024.104885
- Machine Learning Methods for Multiscale Physics and Urban Engineering Problems S. Sharma et al. https://doi.org/10.3390/e24081134
- Structure-based geophysical inversion using implicit geological models A. Balza-Morales et al. https://doi.org/10.1093/gji/ggaf445
- 3D structural and probabilistic modelling of geothermal reservoir horizons in the Northern Eifel and its foreland A. Jüstel et al. https://doi.org/10.1127/zdgg/2025/0436
- The Geodynamic World Builder: A planetary structure creator for the geosciences M. Fraters et al. https://doi.org/10.21105/joss.06671
- Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone S. Brisson et al. https://doi.org/10.1016/j.acags.2023.100115
- Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. https://doi.org/10.1007/s11004-021-09988-0
- 3D structural modeling for seismic exploration based on knowledge graphs X. Zhan et al. https://doi.org/10.1190/geo2020-0924.1
- Geodynamic Modeling With Uncertain Initial Geometries A. Spang et al. https://doi.org/10.1029/2021GC010265
17 citations as recorded by crossref.
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann https://doi.org/10.1016/j.cageo.2025.105911
- Checking the consistency of 3D geological models M. Parquer et al. https://doi.org/10.5194/gmd-18-71-2025
- Application and Validation of PSO-k-Means Clustering for Small-Scale Fault Identification in Coal Mines: A Case Study of the Huainan Coalfield B. Wang et al. https://doi.org/10.1007/s00603-025-04991-x
- Geological Modelling of Urban Environments Under Data Uncertainty C. Ntigkakis et al. https://doi.org/10.3390/geosciences15110423
- Semantic rule-guided three-dimensional reservoir modeling method using an improved multiple-point geostatistics simulation Q. Chen et al. https://doi.org/10.3389/feart.2026.1766398
- Developing a 3D hydrostratigraphical model of the emerged part of the Pelotas Basin along the northern coast of Rio Grande do Sul state, Brazil L. Marquetto et al. https://doi.org/10.1007/s12665-024-11609-y
- Using 3D models to test geological hypotheses for sill complex geometries: Application to Jurassic dolerite intrusions, Tasmania F. Alvarado-Neves et al. https://doi.org/10.1016/j.jsg.2025.105420
- Informed Local Smoothing in 3D Implicit Geological Modeling J. von Harten et al. https://doi.org/10.3390/min11111281
- Incorporating geological structure into sensitivity analysis of subsurface contaminant transport L. Bigler et al. https://doi.org/10.1016/j.advwatres.2024.104885
- Machine Learning Methods for Multiscale Physics and Urban Engineering Problems S. Sharma et al. https://doi.org/10.3390/e24081134
- Structure-based geophysical inversion using implicit geological models A. Balza-Morales et al. https://doi.org/10.1093/gji/ggaf445
- 3D structural and probabilistic modelling of geothermal reservoir horizons in the Northern Eifel and its foreland A. Jüstel et al. https://doi.org/10.1127/zdgg/2025/0436
- The Geodynamic World Builder: A planetary structure creator for the geosciences M. Fraters et al. https://doi.org/10.21105/joss.06671
- Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone S. Brisson et al. https://doi.org/10.1016/j.acags.2023.100115
- Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information C. Kirkwood et al. https://doi.org/10.1007/s11004-021-09988-0
- 3D structural modeling for seismic exploration based on knowledge graphs X. Zhan et al. https://doi.org/10.1190/geo2020-0924.1
- Geodynamic Modeling With Uncertain Initial Geometries A. Spang et al. https://doi.org/10.1029/2021GC010265
Saved (final revised paper)
Latest update: 15 Jun 2026
Short summary
Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Uncertainty is an inherent property of any model of the subsurface. We show how geological...
