Evaluating slope stability uncertainty using coupled Markov chain

Li, Dian Qing; Qi, Xiao; Cao, Zhiyuan; Tang, Xiao Song; Phoon, Kok‐Kwang; Zhou, Chi

doi:10.1016/j.compgeo.2015.11.021

Cited by 100 publications

(32 citation statements)

References 24 publications

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“…The occurrence probability and risk of slope failure are related to various geotechnical uncertainties (e.g., [7,[23][24][25]27,30,34,39,44,45]), among which spatial variability of soil properties is one of the most significant uncertainties affecting slope reliability and risk. Previous studies on slope reliability analysis and risk assessment that account for spatial variability mainly focus on two-dimensional (2-D) analysis, such as Griffiths and Fenton [11], Santoso et al [41], Wang et al [51], Huang et al [17], Zhu et al [54], Li et al [28,29,33,35], Jamshidi Chenari and Alaie [18].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional slope reliability and risk assessment using auxiliary random finite element method

Xiao

Cao

et al. 2016

Computers and Geotechnics

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113

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Section: Introductionmentioning

confidence: 99%

Three-dimensional slope reliability and risk assessment using auxiliary random finite element method

Xiao

Cao

et al. 2016

Computers and Geotechnics

Self Cite

113

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“…However, it requires the marginal distribution of data to be Gaussian. If the data variables are not Gaussian, other outlier detection approaches such as oneclass support vector machine (SVM) (Schölkopf et al, 2001) or isolation forest (Liu et al, 2008) can be used.…”

Section: Falsification Using Multivariate Outlier Detectionmentioning

confidence: 99%

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

2020

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Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

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“…Elfeki and Dekking (2007) used coupled Markov chain (CMC) approach to calibrate hydrogeological lithology model by conditioning on boreholes in the central Rhine-Meuse delta from the Netherlands, and then ran Monte Carlo simulation to re-evaluate the hydrogeological uncertainty. Similar approach was also used by Li et al (2016) to reduce the uncertainty in near-surface geology for the risk assessment of soil slope stability and safety in Western Australia. Jiménez et al (2016) updated 3D hydrogeological models by adding new geological features identified from borehole tracer tests.…”

Section: Introductionmentioning

confidence: 99%

Automated Monte Carlo-based Quantification and Updating of Geological Uncertainty with Borehole Data (AutoBEL v1.0)

Yin¹,

Strebelle²,

Caers³

2019

Preprint

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Abstract. We provide an automated method for uncertainty quantification and updating of geological models using borehole data for subsurface developments (groundwater, geothermal, oil & gas, and CO2 sequestration, etc.) within a Bayesian framework. Our methodologies are developed with the Bayesian Evidential Learning protocol for uncertainty quantification. Under such framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics and fluids), globally and spatially, without time-consuming model re-buildings. To address the above, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a data-scientific method that learns from a series of bijective operations to establish “Bayes-linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allows for fast computation posterior geological models. The proposed framework is completely automated in an opensource project. We demonstrate its application by applying to a generalized synthetic dataset motivated by a gas reservoir from Australia. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction, and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

show abstract

Evaluating slope stability uncertainty using coupled Markov chain

Cited by 100 publications

References 24 publications

Three-dimensional slope reliability and risk assessment using auxiliary random finite element method

Three-dimensional slope reliability and risk assessment using auxiliary random finite element method

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

Automated Monte Carlo-based Quantification and Updating of Geological Uncertainty with Borehole Data (AutoBEL v1.0)

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