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Citation:
　Yuanju Cao,Chao Xu,Jianghui Li,et al.Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar[J].Journal of Marine Science and Application,2024,(3):674-687.[doi:10.1007/s11804-024-00563-7]
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Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar

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Title:
Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar
Author(s):
Yuanju Cao1; 2;  Chao Xu2; 3; 4;  Jianghui Li5;  Tian Zhou2; 3; 4;  Longyue Lin2;  Baowei Chen2; 3; 4
Affilations:
Author(s):
Yuanju Cao1; 2;  Chao Xu2; 3; 4;  Jianghui Li5;  Tian Zhou2; 3; 4;  Longyue Lin2;  Baowei Chen2; 3; 4
1. Southampton Ocean Engineering Joint Institute, Harbin Engineering University, Harbin, 150001, China;
2. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, 150001, China;
3. National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin, 150001, China;
4. Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin, 150001, China;
5. State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen, 361102, China
Keywords:
Carbon capture;  utilization and storage (CCUS)|Gas leakage|Forward-looking sonar|Dual-tree complex wavelet transform (DT-CWT)|Deep learning
分类号:
-
DOI:
10.1007/s11804-024-00563-7
Abstract:
The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.

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Memo

Memo:
Received date:2024-1-15;Accepted date:2024-5-28。
Corresponding author:Longyue Lin,E-mail:linlongyue@hrbeu.edu.cn
Last Update: 2024-09-29