Electrical resistivity tomography with smooth sparse regularization

Zhong, Shichao; Wang, Yibo; Zheng, Yikang; Wu, Shaojiang; Chang, Xu; Zhu, Wei

doi:10.1111/1365-2478.13138

Cited by 5 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the electrodes at positions 3 cm and 11cm served as potential electrodes, while the other two positions were designated as current electrodes. The grounding electrode was placed at the center of the base [36]. The schematic diagram illustrating the arrangement of electrodes is presented in Figure 12.…”

Section: Model Testmentioning

confidence: 99%

Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples

Peng,

Chun,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Research on the electrical properties of rocks and ores plays a crucial role in the development of geophysical electromagnetism methods. However, currently available instruments suffer from high power consumption, a limited number of electrodes, inaccurate measurements, poor portability, and a limited ability to measure the electrical parameters of rocks and ores. To address these issues, this paper presents a three-dimensional electrical impedance tomography system for rock samples with high-density microelectrodes based on an Android system and STM32 microcontroller. The system features high observation accuracy, dense electrode arrays (with 384 current and potential electrodes), flexible electrode selection, user-friendly human–computer interaction, good stability, and real-time performance. Powered by a single power bank, the entire instrument can be controlled and monitored wirelessly via Bluetooth and Wi-Fi technology using an Android smartphone. Additionally, the system not only enables accurate measurement of electrical parameters, but also facilitates the generation of three-dimensional impedance imaging of specimens via inversion algorithms after data export, allowing for a comprehensive understanding of the electrical properties of rocks and ores. This system holds great potential for future research in this field.

show abstract

Section: Model Testmentioning

confidence: 99%

Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples

Peng,

Chun,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The regularization, φ r ( ), is used to incorporate prior knowledge into the imaging process, and it varies in different tasks. For instance, in geophysical or biomedical imaging, to emphasize the sharpness or smoothness of material boundaries, 1 and 2 norms of the spatial gradient of are usually adopted [29]- [33]. In radar imaging, the sparsity of the observed scene is often exploited to improve imaging quality by incorporating sparsity regularization, such as the 1 norm given by φ r ( ) = 1 [34]- [36].…”

Section: Formulations and Challenges Of Em Imagingmentioning

confidence: 99%

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Guo¹,

Huang²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive.Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.

show abstract

A virtual platform of electrical tomography for multiphase flow imaging

2022

View full text Add to dashboard Cite

This paper reports a new approach to quantitatively evaluate the performance of Electrical Tomography (ET) in measuring dynamic multiphase flows. A virtual multiphase flow imaging platform based on ET is constructed and demonstrated on two typical gas-liquid flows, i.e., water-gas flow and oil-gas flow. Two coupling simulation cases, i.e., Water-gas flow field and Electric Currents field Coupling Simulation (WEC-CS) and Oil-gas flow field and ElectroStatics field Coupling Simulation (OES-CS), are performed to simulate multiphase flow sensing of Electrical Impedance Tomography (EIT) and Electrical Capacitance Tomography (ECT). We quantitatively evaluated the representative EIT and ECT image reconstruction algorithms on the virtual evaluation platform bringing evidence of the improved capability to capture the key flow features of the fluid mixture with respect to traditional static panthoms. Ad-hoc treatment of the signal noise enables to better capture dynamic responses of the fluid phases volume fractions and their spatial gradients throughout their mixing along the conduit, ultimately demonstrating unprecedented potential in the quantitative characterization of complex, unsteady multi-phase systems. The proposed image reconstruction constitutes a highly effective platform for quantitative performance evaluation of ET, parameter optimization of model-based ET image reconstruction algorithms, and for the development of data-driven ET algorithms in multiphase flow imaging.

show abstract

Electrical resistivity tomography with smooth sparse regularization

Cited by 5 publications

References 45 publications

Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples

Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples

Physics Embedded Machine Learning for Electromagnetic Data Imaging

A virtual platform of electrical tomography for multiphase flow imaging

Contact Info

Product

Resources

About