Statistical interpretation of spatially varying 2D geo-data from sparse measurements using Bayesian compressive sampling

Zhao, Tengyuan; Hu, Yahong; Wang, Yu

doi:10.1016/j.enggeo.2018.09.022

Cited by 82 publications

(13 citation statements)

References 51 publications

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“…2D Bayesian compressive sampling (BCS) is another key module in the proposed framework for interpolating sparse SPT measurement data along vertical and horizontal directions simultaneously (e.g., Zhao et al 2018), as described in the following section.…”

Section: Random Field Simulation Of Typical Site Conditionsmentioning

confidence: 99%

“…2D BCS is a novel sampling method in signal or image processing which integrates Bayesian formulation with compressive sampling (CS) in a two-dimensional space (e.g., Ji et al 2008Ji et al , 2009Huang et al 2014;Zhao 2016, 2017). In geotechnical engineering, 2D BCS has been used for interpretation of spatial varying 2D geo-data from sparse measurement (e.g., Zhao et al 2018). Let a matrix F represents the spatial variation of SPT data (i.e., 2D…”

Section: D Bayesian Compressive Sampling (Bcs)mentioning

confidence: 99%

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Selection of standard penetration test number for geotechnical investigation of a vertical cross section considering spatial variability and correlation in soil properties

Guan

Wang

2021

Can. Geotech. J.

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Standard Penetration test (SPT) is a widely used in-situ test for characterizing variation of subsurface soil properties, and results of site investigation are usually simplified as a 2D vertical cross-section for subsequent geotechnical design and construction. Current geotechnical design codes and guidelines only provide general recommendations for selection of an appropriate number of in-situ tests (e.g., SPT) (e.g., the greater variability of subsurface conditions, the larger number of SPTs required to obtain sufficient underground information). No quantitative or rational method is available for selecting the appropriate number of SPTs considering spatial variability and correlation in subsurface conditions. A comprehensive parametric study is carried out in this study to investigate the influence of spatial variability in subsurface conditions on the minimum SPT number needed for satisfying an accuracy requirement of site investigation. Random field is adopted to model spatial variation and correlation in soil properties in typical site conditions, and 2D Bayesian compressive sampling is used to interpolate sparse SPT data. Based on the parametric study results, a statistical chart is developed for geotechnical engineers to conveniently select appropriate number of SPTs in a vertical cross-section. Real SPT data from New Zealand are used to illustrate and validate the proposed method.

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Section: Random Field Simulation Of Typical Site Conditionsmentioning

confidence: 99%

Section: D Bayesian Compressive Sampling (Bcs)mentioning

confidence: 99%

Selection of standard penetration test number for geotechnical investigation of a vertical cross section considering spatial variability and correlation in soil properties

Guan

Wang

2021

Can. Geotech. J.

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“…Prior knowledge relevant to the unknown quasi-static profile is obtained from quasi-static CPTu measurements previously acquired in the same offshore area -although at significant distances, i.e., unpaired with the dynamic CPTu that is being transformed. Manipulation and treatment of that information takes place in the frequency domain, using a discrete cosine transform function (i.e., DCT) (Candès et al 2006;Zhao et al 2018;Zhao et al 2020). This avoids the problems of local spatial biases that might be caused by the sharp irregularities that -due to local heterogeneity-are pervasive in CPTu traces.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data

et al. 2023

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Dynamic Cone Penetration Tests (CPTu) profile offshore sediments by impact penetration. To exploit their results in full the measured data is converted to obtain a quasi-static equivalent profile. Dynamic CPTu conversion requires calibrated correction models. Calibration is currently done by using paired (i.e., very close) quasi-static and dynamic tests. It is shown here that paired test data, which may be inconvenient to acquire offshore, are not strictly necessary to convert dynamic CPTu data. A new probabilistic methodology is proposed to call upon quasi-static results from a much wider area in the conversion procedure. Those results feed the prior distribution of a converted profile, within a Bayesian updating scheme where strain rate coefficient and correction model error are also described by updated stochastic variables. The updating scheme is solved numerically using the Transitional Markov Chain MonteCarlo sampling algorithm. To avoid undue influence of local profile heterogeneity, the statistic treatment of the quasi-static CPTu data takes place in the frequency domain, using a discrete cosine transform (DCT). The new procedure is applied to a CPTu campaign offshore Nice (France): dynamic tests are converted with equal precision using quasi-static data acquired at distances orders of magnitude larger than what was previously employed.

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“…This problem can be to some extent addressed using cross-validations, as shown in Lark et al (2013), but the results highly depend on the employed testing data and the quantified uncertainty may not be reliable when the testing data are limited. The statistical interpolation methods include the coupled Markov chain method (Qi et al 2016;Li et al 2019;Liu et al 2020), Markov random field method , Bayesian compressive sampling method Zhao 2016, 2017;Zhao, Hu, and Wang 2018), random field method (Gong et al 2020;Zhao et al 2021), geostatistical methods such as kriging and conditional random field method (Qi et al 2019(Qi et al , 2021a. The coupled Markov chain method can characterize the geological uncertainty using limited borehole data, but it can only be used when the transition of geological types has a Markovian property.…”

Section: Introductionmentioning

confidence: 99%

Prediction of interface of geological formations using generalized additive model

Yang

Chu

2022

Georisk: Assessment and Management of Risk for Engineered Syste

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Geological information such as geological interfaces is important for the design of underground excavation and supporting measures. This in turn requires a method to predict accurately the locations of geological interfaces for the gap areas between boreholes. This study presents a generalized additive model (GAM) to predict the location of the geological interfaces.The performance of the GAM method is evaluated using both simulated data and borehole data for the determination of rockhead in two different geological formations in Singapore. The results show that the GAM method can provide a reasonable confidence interval (CI) of the mean trend and the prediction interval (PI) in the sense that the 95% CI covers about 95% of the actual mean curve while the 95% PI covers around 95% of testing data. Furthermore, the geological complexity can be well reflected as the prediction uncertainty in the geologically complex area is larger than that in the geologically regular area. More importantly, the users can impose prior information or personal judgment regarding the shape of the geological profile on the model. This is an important feature to enable further improvement in the accuracy of the prediction.

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Statistical interpretation of spatially varying 2D geo-data from sparse measurements using Bayesian compressive sampling

Cited by 82 publications

References 51 publications

Selection of standard penetration test number for geotechnical investigation of a vertical cross section considering spatial variability and correlation in soil properties

Selection of standard penetration test number for geotechnical investigation of a vertical cross section considering spatial variability and correlation in soil properties

A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data

Prediction of interface of geological formations using generalized additive model

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