Articles | Volume 9, issue 2
https://doi.org/10.5194/se-9-385-2018
https://doi.org/10.5194/se-9-385-2018
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, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell

Related authors

Topological analysis in Monte Carlo simulation for uncertainty propagation
Evren Pakyuz-Charrier, Mark Jessell, Jérémie Giraud, Mark Lindsay, and Vitaliy Ogarko
Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019,https://doi.org/10.5194/se-10-1663-2019, 2019
Short summary
Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization
Jeremie Giraud, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier
Solid Earth, 10, 193–210, https://doi.org/10.5194/se-10-193-2019,https://doi.org/10.5194/se-10-193-2019, 2019
Short summary

Related subject area

Structural geology
Origin of the Bohai Sea Basin, North China Craton, and implications for bidirectional back-arc extension in the East Asian continental margin
Alan Liu Chen and Xuanhua Chen
Solid Earth, 16, 63–80, https://doi.org/10.5194/se-16-63-2025,https://doi.org/10.5194/se-16-63-2025, 2025
Short summary
Earthquake swarms frozen in an exhumed hydrothermal system (Bolfin Fault Zone, Chile)
Simone Masoch, Giorgio Pennacchioni, Michele Fondriest, Rodrigo Gomila, Piero Poli, José Cembrano, and Giulio Di Toro
Solid Earth, 16, 23–43, https://doi.org/10.5194/se-16-23-2025,https://doi.org/10.5194/se-16-23-2025, 2025
Short summary
Reconciling post-orogenic faulting, paleostress evolution, and structural inheritance in the seismogenic northern Apennines (Italy): insights from the Monti Martani Fault System
Riccardo Asti, Selina Bonini, Giulio Viola, and Gianluca Vignaroli
Solid Earth, 15, 1525–1551, https://doi.org/10.5194/se-15-1525-2024,https://doi.org/10.5194/se-15-1525-2024, 2024
Short summary
Understanding the stress field at the lateral termination of a thrust fold using generic geomechanical models and clustering methods
Anthony Adwan, Bertrand Maillot, Pauline Souloumiac, Christophe Barnes, Christophe Nussbaum, Meinert Rahn, and Thomas Van Stiphout
Solid Earth, 15, 1445–1463, https://doi.org/10.5194/se-15-1445-2024,https://doi.org/10.5194/se-15-1445-2024, 2024
Short summary
Localized shear and distributed strain accumulation as competing shear accommodation mechanisms in crustal shear zones: constraining their dictating factors
Pramit Chatterjee, Arnab Roy, and Nibir Mandal
Solid Earth, 15, 1281–1301, https://doi.org/10.5194/se-15-1281-2024,https://doi.org/10.5194/se-15-1281-2024, 2024
Short summary

Cited articles

Aldiss, D. T., Black, M. G., Entwisle, D. C., Page, D. P., and Terrington, R. L.: Benefits of a 3-D geological model for major tunnelling works: an example from Farringdon, east-central London, UK, Q. J. Eng. Geol. Hydroge., 45, 405–414, https://doi.org/10.1144/qjegh2011-066, 2012.
Allmendinger, R. W., Siron, C. R., and Scott, C. P.: Structural data collection with mobile devices: Accuracy, redundancy, and best practices, J. Struct. Geol., 102, 98–112, 2017.
Aug, C.: Modelisation geologique 3-D et caracterisation des incertitudes par la methode du champ de potentiel, PhD, Ecole des Mines de Paris, Paris, 220 pp., 2004.
Aug, C., Chilès, J.-P., Courrioux, G., and Lajaunie, C.: 3-D geological modelling and uncertainty: The potential-field method, in: Geostatistics Banff 2004, Springer, 145–154, 2005.
Bagchi, P.: Bayesian analysis of directional data, University of Toronto, Ottawa, Ont: National Library of Canada, 1987.
Download
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.
Share