Articles | Volume 10, issue 5
https://doi.org/10.5194/se-10-1663-2019
© Author(s) 2019. 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-10-1663-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Method article
| 
10 Oct 2019
Method article |
| 10 Oct 2019
| 10 Oct 2019
Topological analysis in Monte Carlo simulation for uncertainty propagation
Evren Pakyuz-Charrier
CORRESPONDING AUTHOR
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Intrepid Geophysics, 3 Male Street, Brighton, VIC 3186,
Australia
Mark Jessell
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Jérémie Giraud
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Mark Lindsay
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Vitaliy Ogarko
International Centre for Radio Astronomy Research, The
University of Western Australia, Ken and Julie Michael
Building, 7 Fairway, Crawley, WA 6009, Australia
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- Case Volume Analysis of Neurological Surgery Training Programs in the United States: 2017-2019 B. Hopkins et al. https://doi.org/10.1093/neuopn/okaa017
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- Estimating relative uncertainty of geological 3D models with low density of input data in geologically complex regions F. Staněk et al. https://doi.org/10.1007/s12145-025-01778-0
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- Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis L. Wang et al. https://doi.org/10.1016/j.cageo.2023.105419
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- A time-variant uncertainty propagation analysis method for multimodal probability distributions B. Xie et al. https://doi.org/10.1016/j.probengmech.2025.103840
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- Uncertainty assessment of a 3D geological model by integrating data errors, spatial variations and cognition bias D. Liang et al. https://doi.org/10.1007/s12145-020-00548-4
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23 citations as recorded by crossref.
- Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication L. Pérez-Díaz et al. https://doi.org/10.5194/se-11-889-2020
- 3D structural modeling for seismic exploration based on knowledge graphs X. Zhan et al. https://doi.org/10.1190/geo2020-0924.1
- Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1 A. Schaaf et al. https://doi.org/10.5194/gmd-14-3899-2021
- 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
- Case Volume Analysis of Neurological Surgery Training Programs in the United States: 2017-2019 B. Hopkins et al. https://doi.org/10.1093/neuopn/okaa017
- 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
- Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application J. Giraud et al. https://doi.org/10.5194/se-14-43-2023
- Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models R. Scalzo et al. https://doi.org/10.5194/gmd-15-3641-2022
- Estimating relative uncertainty of geological 3D models with low density of input data in geologically complex regions F. Staněk et al. https://doi.org/10.1007/s12145-025-01778-0
- Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework J. Giraud et al. https://doi.org/10.1190/geo2020-0263.1
- Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code J. Giraud et al. https://doi.org/10.5194/gmd-14-6681-2021
- Trustworthy Uncertainty Quantification Via Distributionally-Robust Dual Adaptive Conformal Prediction Y. Zong et al. https://doi.org/10.1115/1.4069683
- Geomodelamiento 3D con datos escasos en esquema de código abierto: un caso de estudio en la cuenca de los Llanos en Colombia L. Gómez-Santamaría et al. https://doi.org/10.18273/revbol.v48n1-2026004
- Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis L. Wang et al. https://doi.org/10.1016/j.cageo.2023.105419
- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. https://doi.org/10.5194/essd-14-381-2022
- Assessing geometrical uncertainties in geological interface models using Markov chain Monte Carlo sampling via abstract graph J. Huang et al. https://doi.org/10.1016/j.tecto.2023.230032
- A time-variant uncertainty propagation analysis method for multimodal probability distributions B. Xie et al. https://doi.org/10.1016/j.probengmech.2025.103840
- Integration of automatic implicit geological modelling in deterministic geophysical inversion J. Giraud et al. https://doi.org/10.5194/se-15-63-2024
- Quantifying and Analyzing the Uncertainty in Fault Interpretation Using Entropy Z. Lei https://doi.org/10.1007/s11004-024-10156-3
- Machine learning approach for water quality predictions based on multispectral satellite imageries V. Anand et al. https://doi.org/10.1016/j.ecoinf.2024.102868
- Uncertainty assessment of a 3D geological model by integrating data errors, spatial variations and cognition bias D. Liang et al. https://doi.org/10.1007/s12145-020-00548-4
- Computational modeling and analytical validation of singular geometric effects in fault data using a combinatorial approach M. Michalak et al. https://doi.org/10.5194/se-16-1025-2025
- Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging J. Giraud et al. https://doi.org/10.5194/se-11-419-2020
Saved (final revised paper)
Latest update: 01 Oct 2026
Short summary
This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D geological modeling. Topological heterogeneity is observed in the model suite. The study demonstrates that such heterogeneity arises from piecewise nonlinearity inherent to 3-D geological models and contraindicates use of global uncertainty estimation methods. Topological-clustering-driven uncertainty estimation is proposed as a demonstrated alternative to address plausible model heterogeneity.
This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D...
