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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- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. 10.5194/essd-14-381-2022
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- Uncertainty assessment of a 3D geological model by integrating data errors, spatial variations and cognition bias D. Liang et al. 10.1007/s12145-020-00548-4
- Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging J. Giraud et al. 10.5194/se-11-419-2020
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- Uncertainty assessment for 3D geologic modeling of fault zones based on geologic inputs and prior knowledge A. Krajnovich et al. 10.5194/se-11-1457-2020
Latest update: 24 Apr 2024
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...
