Statistical methods to enable practical on-site tomographic imaging of whole-core samples

Mendoza, Alberto; Roininen, Lassi; Girolami, Mark; Heikkinen, Jere; Haario, Heikki

doi:10.1190/geo2018-0436.1

Cited by 5 publications

(10 citation statements)

References 25 publications

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“…For KL expansions, see Berlinet and Thomas-Agnan (2004), and for stable field expansions, see Sullivan (2017). Third option is to use pixel-based approaches (Bolin, 2014;Markkanen et al, 2019;Mendoza et al, 2019), which cover also some α-stable random walks. The family of α-stable processes contains both KL expansions and pixel-based approaches, but one should note that the different approaches lead to different statistical objects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where h is discretization step in both coordinate directions, and λ is the regularization parameter. These priors have been used for X-ray tomography in oil and gas industry (Mendoza et al, 2019) and subsurface imaging (Muhumuza et al, 2020).…”

Section: E10-6mentioning

confidence: 99%

“…Introduction X-ray tomography has applications in various industrial fields such as sawmill industry, where it can be used for detecting knots, rotten parts, and foreign objects in logs (Shustrov et al, 2019;Zolotarev et al, 2019). In oil and gas industry, X-ray tomography can be used to analyze drill-core rock samples for identifying pore structures (Mendoza et al, 2019) and other structural properties of rock. In chemical engineering, X-ray microtomography can be used to measure the internal structure of substances at the micrometer level (Ou et al, 2017).…”

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confidence: 99%

“…We consider a sawmill industry log tomography application for detecting knots, rotten parts, and foreign objects in sawmills (Shustrov et al, 2019;Zolotarev et al, 2019). As an oil and gas industry application, we have drill-core rock sample tomography for identifying pore structures (Mendoza et al, 2019).…”

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Enhancing industrial X-ray tomography by data-centric statistical methods

Suuronen

Emzir

Lasanen

et al. 2020

DCE

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X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, as well as chemical, biomedical, and geotechnical engineering. In this article, we study Bayesian methods for the X-ray tomography reconstruction. In Bayesian methods, the inverse problem of tomographic reconstruction is solved with the help of a statistical prior distribution which encodes the possible internal structures by assigning probabilities for smoothness and edge distribution of the object. We compare Gaussian random field priors, that favor smoothness, to non-Gaussian total variation (TV), Besov, and Cauchy priors which promote sharp edges and high- and low-contrast areas in the object. We also present computational schemes for solving the resulting high-dimensional Bayesian inverse problem with 100,000–1,000,000 unknowns. We study the applicability of a no-U-turn variant of Hamiltonian Monte Carlo (HMC) methods and of a more classical adaptive Metropolis-within-Gibbs (MwG) algorithm to enable full uncertainty quantification of the reconstructions. We use maximum a posteriori (MAP) estimates with limited-memory BFGS (Broyden–Fletcher–Goldfarb–Shanno) optimization algorithm. As the first industrial application, we consider sawmill industry X-ray log tomography. The logs have knots, rotten parts, and even possibly metallic pieces, making them good examples for non-Gaussian priors. Secondly, we study drill-core rock sample tomography, an example from oil and gas industry. In that case, we compare the priors without uncertainty quantification. We show that Cauchy priors produce smaller number of artefacts than other choices, especially with sparse high-noise measurements, and choosing HMC enables systematic uncertainty quantification, provided that the posterior is not pathologically multimodal or heavy-tailed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: E10-6mentioning

confidence: 99%

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confidence: 99%

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Enhancing industrial X-ray tomography by data-centric statistical methods

Suuronen

Emzir

Lasanen

et al. 2020

DCE

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“…X-ray tomography has applications in various industrial fields such as sawmill industry, where it can be used for detecting knots, rotten parts and foreign objects in sawmills (Shustrov et al, 2019;Zolotarev et al, 2019). In oil and gas industry, X-ray tomography can be used to analyze drill-core rock samples to identifying pore structures (Mendoza et al, 2019) and other structural properties of rock. In chemical engineering, X-ray microtomography can be used to measure the internal structure of substances at the micrometer level (Ou et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods

Suuronen¹,

Emzir²,

Lasanen³

et al. 2020

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X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, chemical engineering, and geotechnical engineering. In this article, we study Bayesian methods for the X-ray tomography reconstruction. In Bayesian methods, the inverse problem of tomographic reconstruction is solved with help of a statistical prior distribution which encodes the possible internal structures by assigning probabilities for smoothness and edge distribution of the object. We compare Gaussian random field priors, that favour smoothness, to non-Gaussian total variation, Besov, and Cauchy priors which promote sharp edges and high-contrast and lowcontrast areas in the object. We also present computational schemes for solving the resulting high-dimensional Bayesian inverse problem with 100,000-1,000,000 unknowns. In particular, we study the applicability of a no-U-turn variant of Hamiltonian Monte Carlo methods and of a more classical adaptive Metropolis-within-Gibbs algorithm for this purpose. These methods also enable full uncertainty quantification of the reconstructions. For faster computations, we use maximum a posteriori estimates with limited-memory BFGS optimisation algorithm. As the first industrial application, we consider sawmill industry X-ray log tomography. The logs have knots, rotten parts, and even possibly metallic pieces, making them good examples for non-Gaussian priors. Secondly, we study drill-core rock sample tomography, an example from oil and gas industry. We show that Cauchy priors produce smaller number of artefacts than other choices, especially with sparse high-noise measurements, and choosing Hamiltonian Monte Carlo enables systematic uncertainty quantification. Impact StatementIndustrial X-ray tomography reconstruction accuracy depends on various factors, like the equipment, measurement geometry and constraints of the target. For example dynamical systems are harder targets than static ones. The harder and noisier the setting becomes, the more emphasis goes on mathematical modelling of the targets. Bayesian statistical inversion is a common choice for difficult measurement settings, and its limitations mainly come from the choice of the a priori models. Gaussian models are widely studied, but they provide smooth reconstructions. Total variation priors are not invariant under mesh changes, so doing systematic uncertainty quantification, like data-centric sensor optimisation, cannot be done with them. Besov and Cauchy priors however provide systematic non-Gaussian random field models, which can be used for contrast-boosting tomography. The drawback is higher computational cost. Hence, the techniques developed here are useful for non-time-critical applications with difficult measurement settings. In these cases, the methods developed may provide significantly better reconstructions than the traditional methods, like filtered back-projection.

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