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热带海洋学报

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基于分布式光纤声学传感与深度学习的有效波高远距离估计

廖泽钦1, 2, 罗耀1, 3, 王卫强1, 3, 张建平4, 常叶龙3, 5, 杨晓东1, 6, 张镇秋1, 3, 李骏旻1, 徐康1, 杨积忠7   

  1. 1. 中国科学院南海海洋研究所, 热带海洋环境国家重点实验室, 广东广州 510301;

    2. 中国科学院大学, 北京 100049;

    3. 中国-斯里兰卡联合科教中心, 中国科学院南海海洋研究所, 广东广州 510301;

    4. 大唐汕头新能源有限公司, 广东 汕头 515900;

    5. 华南理工大学, 土木与交通学院, 广东广州 510641;

    6. 中国-巴基斯坦地球科学研究中心, 中国科学院高等教育委员会, 伊斯兰堡 45320, 巴基斯坦;

    7. 同济大学, 海洋地质国家重点实验室, 上海 200092



  • 收稿日期:2026-03-12 修回日期:2026-03-27 接受日期:2026-04-13
  • 通讯作者: Luo Yao
  • 基金资助:

    2024年度中国科学院关键技术人才项目、中国科学院南海海洋研究所发展基金(SCSIO202207)、国家重点研发计划(2022YFE0203500)、广东省科技计划项目(2022B1212050003)及国家自然科学基金(42374136)

Long-distance estimation of significant wave height using distributed acoustic sensing and deep learning

LIAO Zeqin1, 2, LUO Yao1, 3, WANG Weiqiang1, 3, ZHANG Jianping4, CHANG Yelong3, 5, YANG Xiaodong1, 6, ZHANG Zhenqiu1, 3, LI Junmin1, XU Kang1, YANG Jizhong7   

  1. 1. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China

    2. University of Chinese Academy of Sciences, Beijing 100049, China

    3. China-Sri Lanka Joint Center for Education and Research, CAS, South China Sea Institute of Oceanology, Guangzhou, 510301，China.

    4. Datang Shantou New Energy Co., Ltd., Shantou, 515900，China;

    5. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510641, China;

    6. China-Pakistan Joint Research Center on Earth Science, CAS-HEC, Islamabad, 45320, Pakistan

    7. State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China



  • Received:2026-03-12 Revised:2026-03-27 Accepted:2026-04-13
  • Supported by:

    Chinese Academy of Sciences (CAS) Key Technology Talent Program of 2024, Development Fund of South China Sea Institute of Oceanology of Chinese Academy of Sciences (SCSIO202207), National Key R&D Program of China (2022YFE0203500), Science and Technology Planning Project of Guangdong Province, China (2022B1212050003) and the National Natural Science Foundation of China (42374136).

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摘要： 有效波高（Hs）是海洋工程和海上作业中的关键参数，直接影响海上结构物和海岸工程设施的设计与安全评估。目前，Hs的现场观测主要依赖浮标系统和卫星遥感，但这些方法维护成本较高，并且在极端天气条件下往往受到限制。利用海底分布式光纤声学传感（DAS）记录的海洋表面重力波（OSGWs）信号来估计Hs，为解决上述问题提供了一种潜在方案。然而，现有研究大多局限于短距离Hs反演，而基于单通道海底DAS的远距离估计方法精度较低。此外，传统的Hs反演方法未能充分利用DAS多通道感知能力所带来的优势。针对这一问题，本文提出一种基于深度学习的建模方法，从海底多通道DAS记录的有效信号中提取时空特征，实现对远海区域（约17.8km处）声学波浪流速剖面仪（AWAC）观测的Hs的反演。结果表明，该方法显著提高了远距离Hs估计的精度，并扩展了海底DAS反演Hs的有效范围，展示了其在海洋工程应用中实现低成本、可靠的海洋表面重力波参数反演的潜力。

关键词: 有效波高, 分布式光纤声学传感, 深度学习, 远距离估计

Abstract: Significant wave height (Hs) is a key parameter in ocean engineering and marine operations, directly influencing the design and safety assessment of offshore structures and coastal facilities. Current in-situ Hs observations primarily rely on buoys and satellite remote sensing, which are costly to maintain and often limited by extreme weather conditions. The technique for estimating Hs from ocean surface gravity waves (OSGWs) recorded by seafloor distributed acoustic sensing (DAS) offers a potential solution to these challenges. However, existing studies are mostly limited to short-range Hs inversion, and approaches using single-channel seafloor DAS for long-range estimation suffer from low accuracy. The previous approach for Hs inversion does not fully utilize the advantages offered by the multi-channel sensing capability of the DAS. To address this issue, we propose a deep learning-based modeling approach to extract spatiotemporal features from the effective DAS signals recorded by multiple seafloor DAS channels, enabling the inversion of Hs observed by an Acoustic Wave and Current Profiler (AWAC) in the distant ocean area (~17.8km). Our method significantly improves the accuracy of long-range Hs estimation and extends the effective range of seafloor DAS-based Hs retrieval, highlighting its potential for cost-effective and reliable OSGW parameter retrieval in ocean engineering applications.

Key words: Significant wave height, Distributed acoustic sensing, Deep learning, Long-Distance Estimation