Articles | Volume 9, issue 2
Solid Earth, 9, 385–402, 2018
https://doi.org/10.5194/se-9-385-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Solid Earth, 9, 385–402, 2018
https://doi.org/10.5194/se-9-385-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Method article
06 Apr 2018
Method article
| 06 Apr 2018
Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization
Evren Pakyuz-Charrier et al.
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- Combining implicit geological modeling, field surveys, and hydrogeological modeling to describe groundwater flow in a karst aquifer F. D’Affonseca et al. 10.1007/s10040-020-02220-z
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- Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure K. Sharma et al. 10.1109/ACCESS.2021.3083969
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- Constraining uncertainty of fault orientation using a combinatorial algorithm M. Michalak et al. 10.1016/j.cageo.2021.104777
- Boundary simulation – a hierarchical approach for multiple categories F. Amarante et al. 10.1080/25726838.2021.1889295
- Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms X. Wei & J. Sun 10.1190/geo2020-0672.1
- 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
- Modeling uncertain data using Monte Carlo integration method for clustering K. Sharma & A. Seal 10.1016/j.eswa.2019.06.050
- Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging J. Giraud et al. 10.5194/se-11-419-2020
- Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models H. Olierook et al. 10.1016/j.gsf.2020.04.015
- Explicit-implicit-integrated 3-D geological modelling approach: A case study of the Xianyan Demolition Volcano (Fujian, China) J. Guo et al. 10.1016/j.tecto.2020.228648
- Uncertainty Visualisation of a 3D Geological Geometry Model and Its Application in GIS-Based Mineral Resource Assessment: A Case Study in Huayuan District, Northwestern Hunan Province, China N. Li et al. 10.1007/s12583-021-1434-y
- The role of geological models and uncertainties in safety assessments M. Bjorge et al. 10.1007/s12665-022-10305-z
- Multi-view spectral clustering for uncertain objects K. Sharma & A. Seal 10.1016/j.ins.2020.08.080
- Constraining 3D geometric gravity inversion with a 2D reflection seismic profile using a generalized level set approach: application to the eastern Yilgarn Craton M. Rashidifard et al. 10.5194/se-12-2387-2021
- Regional groundwater flow and karst evolution–theoretical approach and example from Switzerland S. Scheidler et al. 10.1007/s12665-021-09471-3
- Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data V. Ogarko et al. 10.1190/geo2019-0633.1
- A simulation-based framework for modulating the effects of subjectivity in greenfield Mineral Prospectivity Mapping with geochemical and geological data M. Parsa & A. Pour 10.1016/j.gexplo.2021.106838
- Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code J. Giraud et al. 10.5194/gmd-14-6681-2021
- Concentration-distance from centroids (C-DC) multifractal modeling: A novel approach to characterizing geochemical patterns based on sample distance from mineralization B. Sadeghi & D. Cohen 10.1016/j.oregeorev.2021.104302
- Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis J. Giraud et al. 10.1093/gji/ggz152
- Using Delaunay triangulation and cluster analysis to determine the orientation of a sub-horizontal and noise including contact in Kraków-Silesian Homocline, Poland M. Michalak et al. 10.1016/j.cageo.2019.104322
- Automated geological map deconstruction for 3D model construction using <i>map2loop</i> 1.0 and <i>map2model</i> 1.0 M. Jessell et al. 10.5194/gmd-14-5063-2021
- Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success R. Scalzo et al. 10.5194/gmd-12-2941-2019
- 3D geological structure inversion from Noddy-generated magnetic data using deep learning methods J. Guo et al. 10.1016/j.cageo.2021.104701
- Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework J. Giraud et al. 10.1190/geo2020-0263.1
- Topological analysis in Monte Carlo simulation for uncertainty propagation E. Pakyuz-Charrier et al. 10.5194/se-10-1663-2019
- Outlier-robust multi-view clustering for uncertain data K. Sharma & A. Seal 10.1016/j.knosys.2020.106567
- Concentration-concentration fractal modelling: A novel insight for correlation between variables in response to changes in the underlying controlling geological-geochemical processes B. Sadeghi 10.1016/j.oregeorev.2020.103875
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- Multi‐scenario Interpretations From Sparse Fault Evidence Using Graph Theory and Geological Rules G. Godefroy et al. 10.1029/2020JB020022
- Reproducibility of atomistic friction computer experiments: a molecular dynamics simulation study S. Stephan et al. 10.1080/08927022.2021.1987430
- Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning A. Schaaf & C. Bond 10.5194/se-10-1049-2019
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Discussed (preprint)
Latest update: 16 May 2022
Short summary
MCUE is a method that produces probabilistic 3-D geological models by sampling from distributions that represent the uncertainty of the initial input dataset. This process generates numerous plausible datasets used to produce a range of statistically plausible 3-D models which are combined into a single probabilistic model. In this paper, improvements to distribution selection and parameterization for input uncertainty are proposed.
MCUE is a method that produces probabilistic 3-D geological models by sampling from...
