Electrical resistance tomography on two-phase flow based on compressed sensing theory

Jingfu Yan; Yu Cao; Xueshi Chen

doi:10.1117/12.3012213

21 December 2023 Electrical resistance tomography on two-phase flow based on compressed sensing theory

Jingfu Yan, Yu Cao, Xueshi Chen

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129702Z (2023) https://doi.org/10.1117/12.3012213
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Considering the problem that the imaging accuracy by traditional algorithms for two-phase flow is not ideal, this paper produces the electrical resistance tomography (ERT) algorithms based on compressed sensing (CS) theory, which focuses on three key issues of signal sparse representation, observation matrix optimization and signal reconstruction. Specifically, in order to improve the signal sparsity, Haar wavelets are adopted as the sparse basis, and the pixel index prior information is introduced. To make the sensitivity matrix in ERT better meet the CS theory requirement, SVD optimization is conducted. As for the imaging algorithm, the objective function based on the regularization items of L1 or L0 is selectively used according to the two-phase flow pattern. The experimental results indicate that compared with the commonly used image reconstruction algorithms such as Tikhonov algorithm, Landweber algorithm, and CG algorithm, the algorithm proposed in this paper has more comprehensive and balanced performance, and can be used as an efficient image reconstruction solution of ERT for two-phase flow.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingfu Yan, Yu Cao, and Xueshi Chen "Electrical resistance tomography on two-phase flow based on compressed sensing theory", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129702Z (21 December 2023); https://doi.org/10.1117/12.3012213

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