Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration
This study focuses on the application of electrical resistivity tomography (ERT) for monitoring the growth process of CO<sub>2</sub> hydrate in subsea carbon sequestration, aiming to provide technical support for the safety assessment of marine carbon storage. By designing single-target,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1205 |
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| _version_ | 1839615824670752768 |
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| author | Zitian Lin Qia Wang Shufan Li Xingru Li Jiajie Ye Yidi Zhang Haoning Ye Yangmin Kuang Yanpeng Zheng |
| author_facet | Zitian Lin Qia Wang Shufan Li Xingru Li Jiajie Ye Yidi Zhang Haoning Ye Yangmin Kuang Yanpeng Zheng |
| author_sort | Zitian Lin |
| collection | DOAJ |
| description | This study focuses on the application of electrical resistivity tomography (ERT) for monitoring the growth process of CO<sub>2</sub> 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 CO<sub>2</sub> hydrate reservoir characteristics and provides reliable data support for the safety assessment of hydrate reservoirs. |
| format | Article |
| id | doaj-art-e05a6fb065ac4fc08be17eec9d1b0719 |
| institution | Matheson Library |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e05a6fb065ac4fc08be17eec9d1b07192025-07-25T13:26:46ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01137120510.3390/jmse13071205Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon SequestrationZitian Lin0Qia Wang1Shufan Li2Xingru Li3Jiajie Ye4Yidi Zhang5Haoning Ye6Yangmin Kuang7Yanpeng Zheng8College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaThis study focuses on the application of electrical resistivity tomography (ERT) for monitoring the growth process of CO<sub>2</sub> 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 CO<sub>2</sub> hydrate reservoir characteristics and provides reliable data support for the safety assessment of hydrate reservoirs.https://www.mdpi.com/2077-1312/13/7/1205offshore carbon sequestrationCO<sub>2</sub> hydrateselectrical resistivity tomographyimage reconstructionneural networks |
| spellingShingle | Zitian Lin Qia Wang Shufan Li Xingru Li Jiajie Ye Yidi Zhang Haoning Ye Yangmin Kuang Yanpeng Zheng Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration Journal of Marine Science and Engineering offshore carbon sequestration CO<sub>2</sub> hydrates electrical resistivity tomography image reconstruction neural networks |
| title | Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration |
| title_full | Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration |
| title_fullStr | Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration |
| title_full_unstemmed | Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration |
| title_short | Electrical Resistivity Tomography Methods and Technical Research for Hydrate-Based Carbon Sequestration |
| title_sort | electrical resistivity tomography methods and technical research for hydrate based carbon sequestration |
| topic | offshore carbon sequestration CO<sub>2</sub> hydrates electrical resistivity tomography image reconstruction neural networks |
| url | https://www.mdpi.com/2077-1312/13/7/1205 |
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