Journal cover Journal topic
Solid Earth An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 3.495 IF 3.495
  • IF 5-year<br/> value: 3.386 IF 5-year
    3.386
  • CiteScore<br/> value: 3.70 CiteScore
    3.70
  • SNIP value: 0.783 SNIP 0.783
  • SJR value: 1.039 SJR 1.039
  • IPP value: 1.987 IPP 1.987
  • h5-index value: 20 h5-index 20
https://doi.org/10.5194/se-2017-115
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Method article
17 Oct 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Solid Earth (SE).
Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization
Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley WA 6009 Australia
Abstract. Three-dimensional (3D) geological modeling aims to determine geological information in a 3D space using structural data (foliations and interfaces) and topological rules as inputs. They are necessary in any project where the properties of the subsurface matters, they express our understanding of geometries in depth. For that reason, 3D geological models have a wide range of practical applications including but not restrained to civil engineering, oil and gas industry, mining industry and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator’s parameterization) combined with input uncertainty (observational-, conceptual- and technical errors). Because 3D geological models are often used for impactful decision making it is critical that all 3D geological models provide accurate estimates of uncertainty. This paper’s focus is set on the effect of structural input data uncertainty propagation in implicit 3D geological modeling using GeoModeller API. This aim is achieved using Monte Carlo simulation uncertainty estimation (MCUE), a heuristic stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of inappropriate disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e. scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminishes the occurrence of artefacts.

Citation: Pakyuz-Charrier, E., Lindsay, M., Ogarko, V., Giraud, J., and Jessell, M.: Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization, Solid Earth Discuss., https://doi.org/10.5194/se-2017-115, in review, 2017.
Evren Pakyuz-Charrier et al.
Evren Pakyuz-Charrier et al.

Data sets

Basic graben GeoModeller model and relevant MCUE outputs
E. Pakyuz-Charrier
https://doi.org/10.5281/zenodo.854730
Mansfield (Victoria, Australia) area original GeoModeller model and relevant MCUE outputs
E. Pakyuz-Charrier and Intrepid Geophysics
https://doi.org/10.5281/zenodo.848225
Evren Pakyuz-Charrier et al.

Viewed

Total article views: 276 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
223 49 4 276 0 0

Views and downloads (calculated since 17 Oct 2017)

Cumulative views and downloads (calculated since 17 Oct 2017)

Viewed (geographical distribution)

Total article views: 276 (including HTML, PDF, and XML)

Thereof 270 with geography defined and 6 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 24 Nov 2017
Publications Copernicus
Download
Short summary
MCUE is a method that produces probabilistic 3D 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 3D 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 3D geological models by sampling from distributions...
Share