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Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar 认领 引用 被引量：2
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作者 Yuanju Cao Chao Xu +3 位作者 Jianghui Li Tian Zhou Longyue Lin Baowei Chen 《哈尔滨工程大学学报（英文版）》 CSCD 2024年第3期674-687,共14页
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 ... 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. 展开更多
关键词 Carbon capture utilization and storage(CCUS) Gas leakage Forward-looking sonar Dual-tree complex wavelet transform(DT-CWT) Deep learning
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