首页|Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar

Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar

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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.

Carbon capture,utilization and storage(CCUS)Gas leakageForward-looking sonarDual-tree complex wavelet transform(DT-CWT)Deep learning

Yuanju Cao、Chao Xu、Jianghui Li、Tian Zhou、Longyue Lin、Baowei Chen

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Southampton Ocean Engineering Joint Institute,Harbin Engineering University,Harbin 150001,China

College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China

National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin 150001,China

Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technology,Harbin 150001,China

State Key Laboratory of Marine Environmental Science,College of Ocean and Earth Sciences,Xiamen University,Xiamen 361102,China

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2024

10.1007/s11804-024-00563-7

哈尔滨工程大学学报(英文版)
哈尔滨工程大学

哈尔滨工程大学学报(英文版)

CSTPCD
影响因子：0.381
ISSN：1671-9433
年,卷(期)：2024.23(3)