Physical-property-, lithology- and surface-geometry-based joint inversion using Pareto Multi-Objective Global Optimization

Bijani, Rodrigo; Lelièvre, Peter G.; Ponte-Neto, Cosme F.; Farquharson, Colin G.

doi:10.1093/gji/ggx046

Cited by 30 publications

(16 citation statements)

References 61 publications

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“…More recently, Li (2012, 2016) have introduced clustering algorithms as a means of enforcing similarities between the inverted model and prior petrophysical data. Zhang and Revil (2015), and more recently Bijani et al (2017) and Giraud et al (2016 and integrate petrophysics and geology in joint inversion and demonstrate the advantages of integration and joint inversion. However, even if the goal of the aforementioned methods is to obtain better-constrained models, little work has been done to quantify the impact of uncertainty in non-geophysical sources of information used to derive constraints accounting for geological information (Reid et al, 2013).…”

Section: Introductionmentioning

confidence: 88%

Impact of uncertain geology in constrained geophysical inversion

Giraud

Pakyuz-Charrier

Ogarko

et al. 2018

ASEG Extended Abstracts

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The integration of geological modelling, petrophysics and geophysics in a single inversion scheme is a complex and powerful strategy for solving challenges faced in geoscientific resource exploration. Probabilistic geological modelling and geophysical inversion are non-linear processes that show various degrees of sensitivity to uncertainty in input measurements. Using field geological measurements from the Mansfield area (Victoria, Australia) and synthetic geophysical data, we present a synthetic case study investigating the impact of geological uncertainty on constrained joint geophysical inversion. We investigate the influence of uncertain geological measurements on geologically constrained inversion through a sensitivity analysis to uncertainty in orientation data. Probabilistic geological models used to define constraints for geophysical joint inversion are obtained through a Monte-Carlo based uncertainty estimation (MCUE) method. We simulate a broad range of possible cases through a parameter sweep on uncertainty levels in geological measurements to provide a reference for practitioners. The analysis and comparison of the results at varying uncertainty levels show that results can be grouped into two main categories. For the highest uncertainty levels, significant portions of the models retain the characteristic features of geologically unconstrained inversions. Meanwhile, below a threshold in uncertainty level, inversion benefits from the interaction of geophysical data and geologically conditioned constraints. In such cases, inverted models are improved compared to both the geological modelling alone and geologically unconstrained inversion. The conclusion of this work is that knowledge of this threshold is critical for the interpretation of results and decision making because it indicates whether the datasets provide enough information to take advantage of the complementarities between geological modelling and geophysical inversion. Knowledge of this threshold can also support decision making pertaining to inversion strategies and geological field data collection.

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Section: Introductionmentioning

confidence: 88%

Impact of uncertain geology in constrained geophysical inversion

Giraud

Pakyuz-Charrier

Ogarko

et al. 2018

ASEG Extended Abstracts

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“…Furthermore, the results of these analyses can be fed to external validation systems to reduce geological uncertainty and improve understanding of the modeled volume. Examples of external validation systems include geophysical inversion (Giraud et al, 2019), concurrent geophysical forward modeling (Bijani et al, 2017;Lipari et al, 2017), 3-D restoration, fluid flow simulations or ground truthing. Lastly, the results obtained from the external validation systems may be reutilized by MCUP to further refine the models.…”

Section: Comparative Analysismentioning

confidence: 99%

Topological analysis in Monte Carlo simulation for uncertainty propagation

et al. 2019

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Abstract. This paper proposes and demonstrates improvements for the Monte Carlo simulation for uncertainty propagation (MCUP) method. MCUP is a type of Bayesian Monte Carlo method aimed at input data uncertainty propagation in implicit 3-D geological modeling. In the Monte Carlo process, a series of statistically plausible models is built from the input dataset of which uncertainty is to be propagated to a final probabilistic geological model or uncertainty index model. Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUP. These differences are interpreted as analogous to population heterogeneity. The source of this heterogeneity is traced to be the non-linear relationship between plausible datasets' variability and plausible model's variability. Non-linearity is shown to mainly arise from the effect of the geometrical rule set on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces topological heterogeneity and challenges the underlying assumption of homogeneity which global uncertainty estimates rely on. To address this issue, a method for topological analysis applied to the plausible model suite in MCUP is introduced. Boolean topological signatures recording lithological unit adjacency are used as n-dimensional points to be considered individually or clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic examples with varying levels of confidence in the structural input data. Results indicate that topological signatures constitute a powerful discriminant to address plausible model heterogeneity. Basic topological signatures appear to be a reliable indicator of the structural behavior of the plausible models and provide useful geological insights. Moreover, ignoring heterogeneity was found to be detrimental to the accuracy and relevance of the probabilistic geological models and uncertainty index models. Highlights. Monte Carlo uncertainty propagation (MCUP) methods often produce topologically distinct plausible models. Plausible models can be differentiated using topological signatures. Topologically similar probabilistic geological models may be obtained through topological signature clustering.

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“…uncertainty and improve understanding of the modelled volume. Examples of external validation systems include geophysical inversion (Giraud et al, 2019), concurrent geophysical forward modeling (Bijani et al, 2017;Lipari et al, 2017), or groundtruthing.…”

Section: Comparative Analysismentioning

confidence: 99%

Topological Analysis in Monte Carlo Simulation for Uncertainty Estimation

Pakyuz-Charrier¹,

Jessell²,

Giraud³

et al. 2019

Preprint

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Abstract. This paper proposes and demonstrates improvements for the Monte Carlo simulation for Uncertainty Estimation (MCUE) method. MCUE is a type of Bayesian Monte Carlo aimed at input data uncertainty propagation in implicit 3D geological modeling. In the Monte Carlo process, a series of statistically plausible models are built from the input data set which uncertainty is to be propagated to a final probabilistic geological model (PGM) or uncertainty index model (UIM). Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUE. These differences are interpreted as analogous to population heterogeneity. The source of this heterogeneity is traced to be the non-linear relationship between plausible datasets’ variability and plausible model’s variability. Non-linearity is shown to arise from the effect of the geometrical ruleset on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces geological incompatibility and challenges the underlying assumption of homogeneity which global uncertainty estimates rely on. To address this issue, a method for topological analysis applied to the plausible model suite in MCUE is introduced. Boolean topological signatures recording lithological units’ adjacency are used as n-dimensional points to be considered individually or clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic examples with varying levels of confidence in the structural input data. Results indicate that topological signatures constitute a powerful discriminant to address plausible model heterogeneity. Basic topological signatures appear to be a reliable indicator of the structural behavior of the plausible models and provide useful geological insights. Moreover, ignoring heterogeneity was found to be detrimental to the accuracy and relevance of the PGMs and UIMs.

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Physical-property-, lithology- and surface-geometry-based joint inversion using Pareto Multi-Objective Global Optimization

Cited by 30 publications

References 61 publications

Impact of uncertain geology in constrained geophysical inversion

Impact of uncertain geology in constrained geophysical inversion

Topological analysis in Monte Carlo simulation for uncertainty propagation

Topological Analysis in Monte Carlo Simulation for Uncertainty Estimation

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