Articles | Volume 9, issue 2
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

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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,, 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.
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.