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

作     者：Yuanju Cao Chao Xu Jianghui Li Tian Zhou Longyue Lin Baowei Chen 曹元驹;徐超;李姜辉;周天;林龙岳;陈宝伟

作者机构：Southampton Ocean Engineering Joint InstituteHarbin Engineering UniversityHarbin 150001China College of Underwater Acoustic EngineeringHarbin Engineering UniversityHarbin 150001China National Key Laboratory of Underwater Acoustic TechnologyHarbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University)Ministry of Industry and Information TechnologyHarbin 150001China State Key Laboratory of Marine Environmental ScienceCollege of Ocean and Earth SciencesXiamen UniversityXiamen 361102China 

出 版 物：《哈尔滨工程大学学报（英文版）》 (Journal of Marine Science and Application)

年 卷 期：2024年第23卷第3期

页      面：674-687页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 0824[工学-船舶与海洋工程] 

主　　题：Carbon capture utilization and storage(CCUS) Gas leakage Forward-looking sonar Dual-tree complex wavelet transform(DT-CWT) Deep learning 

摘      要：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.