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Volume 13
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10.3390/jmse13071205
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration

by
Zitian Lin
1,
Qia Wang
1,
Shufan Li
1,
Xingru Li
1,
Jiajie Ye
1,
Yidi Zhang
1,
Haoning Ye
1,
Yangmin Kuang
1,2,3,* and
Yanpeng Zheng
1,2,3,*
1
College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, China
2
Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China
3
Laboratory for Marine Geology, Qingdao Marine Science and Technology Center, Qingdao 266237, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(7), 1205; https://doi.org/10.3390/jmse13071205 (registering DOI)
Submission received: 8 May 2025 / Revised: 13 June 2025 / Accepted: 16 June 2025 / Published: 21 June 2025
(This article belongs to the Special Issue Advances in Marine Gas Hydrate Exploration and Discovery—2nd Edition)
Versions Notes

Abstract

This study focuses on the application of electrical resistivity tomography (ERT) for monitoring the growth process of CO2 hydrate in subsea carbon sequestration, aiming to provide technical support for the safety assessment of marine carbon storage. By designing single-target, dual-target, and multi-target hydrate samples, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and residual neural networks (ResNets) were constructed and compared with traditional image reconstruction algorithms (e.g., back-projection) to quantitatively analyze ERT imaging accuracy. The experiments used boundary voltage as the input and internal conductivity distribution as the output, employing the relative image error (RIE) and image correlation coefficient (ICC) to evaluate algorithmic performance. The results demonstrate that neural network algorithms—particularly RNNs—exhibit superior performance compared to traditional image reconstruction methods due to their strong noise resistance and nonlinear mapping capabilities. These algorithms significantly improve the edge clarity in target identification, enabling the precise capture of the hydrate distribution during carbon sequestration. This advancement effectively enhances the monitoring capability of CO2 hydrate reservoir characteristics and provides reliable data support for the safety assessment of hydrate reservoirs.
Keywords: offshore carbon sequestration; CO2 hydrates; electrical resistivity tomography; image reconstruction; neural networks offshore carbon sequestration; CO2 hydrates; electrical resistivity tomography; image reconstruction; neural networks

Share and Cite

MDPI and ACS Style

Lin, Z.; Wang, Q.; Li, S.; Li, X.; Ye, J.; Zhang, Y.; Ye, H.; Kuang, Y.; Zheng, Y. Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration. J. Mar. Sci. Eng. 2025, 13, 1205. https://doi.org/10.3390/jmse13071205

AMA Style

Lin Z, Wang Q, Li S, Li X, Ye J, Zhang Y, Ye H, Kuang Y, Zheng Y. Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration. Journal of Marine Science and Engineering. 2025; 13(7):1205. https://doi.org/10.3390/jmse13071205

Chicago/Turabian Style

Lin, Zitian, Qia Wang, Shufan Li, Xingru Li, Jiajie Ye, Yidi Zhang, Haoning Ye, Yangmin Kuang, and Yanpeng Zheng. 2025. "Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration" Journal of Marine Science and Engineering 13, no. 7: 1205. https://doi.org/10.3390/jmse13071205

APA Style

Lin, Z., Wang, Q., Li, S., Li, X., Ye, J., Zhang, Y., Ye, H., Kuang, Y., & Zheng, Y. (2025). Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration. Journal of Marine Science and Engineering, 13(7), 1205. https://doi.org/10.3390/jmse13071205

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