Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods
| Autor: | Sari Lasanen, Lassi Roininen, Muhammad F. Emzir, Simo Särkkä, Jarkko Suuronen |
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| Přispěvatelé: | LUT University, Department of Electrical Engineering and Automation, University of Oulu, Aalto-yliopisto, Aalto University |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science 010103 numerical & computational mathematics 01 natural sciences non-Gaussian random fields Gaussian random field Computational Engineering Finance and Science (cs.CE) Methodology (stat.ME) 010104 statistics & probability contrast-boosting inversion Prior probability Maximum a posteriori estimation FOS: Electrical engineering electronic engineering information engineering Hamiltonian Monte Carlo 0101 mathematics Uncertainty quantification Computer Science - Computational Engineering Finance and Science Statistics - Methodology Tomographic reconstruction Image and Video Processing (eess.IV) industrial X-ray tomography Cauchy distribution Inverse problem Electrical Engineering and Systems Science - Image and Video Processing Bayesian statistical inverse problems Broyden–Fletcher–Goldfarb–Shanno algorithm Algorithm |
| DOI: | 10.48550/arxiv.2003.03814 |
| Popis: | Funding Information: This work has been funded by Academy of Finland (project numbers 326240, 326341, 314474, 321900, 313708) and by European Regional Development Fund (ARKS project A74305). Publisher Copyright: © The Author(s), 2020. 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. |
| Databáze: | OpenAIRE |
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