Geological modelling from field data and geological knowledge

Guillén, A.; Calcagno, Philippe; Courrioux, Gabriel; Joly, Aurore; Ledru, Patrick

doi:10.1016/j.pepi.2008.06.014

Cited by 158 publications

(31 citation statements)

References 18 publications

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“…The corresponding data misfit maps show a linear correlation coefficient of 0.999 (see details in Appendix A). This similarity illustrates that, as in many other studies, most changes related to holistic data integration in geophysical inversion occur primarily in model space, hence reducing the effect of non-uniqueness (Abtahi et al, 2016;Abubakar et al, 2012;Brown et al, 2012;Demirel and Candansayar, 2017;Gallardo et al, 2012;Gallardo and Meju, 2004, 2011Gao et al, 2012;Giraud et al, 2017;Guo et al, 2017;Heincke et al, 2017;Jardani et al, 2013;Juhojuntti and Kamm, 2015;Kalscheuer et al, 2015;Molodtsov et al, 2013;Moorkamp et al, 2013;Rittgers et al, 2016;Li, 2016, 2017).…”

Section: Resultssupporting

confidence: 79%

“…The integration methodology we develop is similar in philosophy to the work of Brown et al (2012), Guo et al (2017) and Wiik et al (2015), who extract continuous structural information from seismic data to adjust the strength of the regularization term locally in order to promote specific structural features during electromagnetic inversion. However, our work differs from these authors in four main respects.…”

Section: Introductionmentioning

confidence: 99%

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Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

et al. 2019

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Abstract. We introduce a workflow integrating geological modelling uncertainty information to constrain gravity inversions. We test and apply this approach to the Yerrida Basin (Western Australia), where we focus on prospective greenstone belts beneath sedimentary cover. Geological uncertainty information is extracted from the results of a probabilistic geological modelling process using geological field data and their inferred accuracy as inputs. The uncertainty information is utilized to locally adjust the weights of a minimum-structure gradient-based regularization function constraining geophysical inversion. Our results demonstrate that this technique allows geophysical inversion to update the model preferentially in geologically less certain areas. It also indicates that inverted models are consistent with both the probabilistic geological model and geophysical data of the area, reducing interpretation uncertainty. The interpretation of inverted models reveals that the recovered greenstone belts may be shallower and thinner than previously thought.

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Section: Resultssupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

et al. 2019

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“…The co-kriging algorithm used in GeoModeller interpolates a 3-D vector field and converts it into a potential (scalar) field Guillen et al, 2008) that is then contoured to draw interface surfaces. The space between surfaces is defined as belonging to a specific unit based on topological rules.…”

Section: Appendix C: Co-kriging Algorithm In Geomodellermentioning

confidence: 99%

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

et al. 2018

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Abstract. Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the 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) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a 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 3-D 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 incorrect 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 diminish the occurrence of artifacts.

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“…The co-Kriging algorithm used in GeoModeller interpolates a 3D vector field and converts it into a potential (scalar) field Guillen et al 2008) that is then contoured to draw interface surfaces. The space between surfaces is defined as belonging to a specific unit based on topological rules.…”

Section: Appendix C: Co-kriging Algorithm In Geomodellermentioning

confidence: 99%

Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

Pakyuz-Charrier¹,

Lindsay²,

Ogarko

et al. 2017

Preprint

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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 10 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 15Carlo 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 20 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 25 that the proposed workflows improve the reliability of uncertainty estimation and diminishes the occurrence of artefacts.Solid Earth Discuss., https://doi

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Geological modelling from field data and geological knowledge

Cited by 158 publications

References 18 publications

Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

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