1.自然资源部第一海洋研究所;2.青岛大学 计算机科学技术学院;3.中国石油大学（华东） 海洋与空间信息学院
国家自然科学基金项目“基于海洋表层卫星遥感观测的海洋水下动力环境智能探测方法研究”(62231028)。
1.First Institute of Oceanography,Ministry of Natural Resources;2.College of Computer Science Technology,Qingdao University;3.College of Oceanography and Space Informatics，China University of Petroleum (East China)
海洋三维温盐场信息是描述海洋物理属性特征和掌握海洋物理运动过程的重要参数,获取准确的海洋三维温度和盐度信息对于认识海洋、开发利用海洋和海洋科学研究等具有重要意义。随着人工智能与深度学习方法的发展,采用深度学习方法的海洋次表层三维温盐场智能探测研究成为热点之一。本文从海洋温盐观测数据集、传统机器学习方法三维温盐智能探测、一般神经网络三维温盐智能探测和深度学习三维温盐智能探测等方面展开,对与海洋三维温盐场智能探测相关的研究进展进行综述,论文最后针对三维温盐场智能探测存在的问题和未来的发展趋势进行了总结和展望。
The three-dimensional temperature and salinity field information of the ocean is an important parameter for describing the physical properties of the ocean and mastering the process of ocean physical movement. Obtaining accurate three-dimensional temperature-salinity information of the ocean is of great significance for understanding the ocean, developing and utilizing the ocean, and marine scientific research. With the development of artificial intelligence and deep learning methods, the intelligent detection of subsurface three-dimensional temperature and salinity fields using deep learning methods has become one of the hotspots. This article reviews the research progress related to the intelligent detection of ocean three-dimensional temperature and salinity fields from the perspectives of ocean temperature and salinity observation datasets, traditional machine learning methods for three-dimensional temperature and salinity intelligent detection, general neural network three-dimensional temperature and salinity intelligent detection, and deep learning three-dimensional temperature and salinity intelligent detection. Finally, the paper summarizes and looks forward to the problems and future development trends of intelligent detection of three-dimensional temperature and salinity fields.
