Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the “AI ocean era”. In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies.

中文翻译：

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深度学习在物理海洋学中的应用：综合综述


深度学习作为一种数据驱动技术，近年来随着物联网大数据、机器学习算法和计算硬件的快速发展，引起了各学科的广泛关注。事实证明，它在各个领域的现有应用中以更灵活的方式获得了与传统方法相当甚至更准确的结果。在物理海洋学这一海洋学的重要科学领域中，丰富的海面数据和高度的动态复杂性为深度学习的广泛应用铺平了道路。此外，研究人员在海洋环流、海洋动力学、海洋气候、海洋遥感、海洋地球物理等领域对传统方法进行了大量创新工作，引领海洋学研究进入“人工智能海洋时代”。在我们的研究中，我们将物理海洋学的众多研究主题分为四个方面：表层要素、次表层要素、典型海洋现象以及典型天气和气候现象。我们回顾了过去三年来深度学习在物理海洋学中的前沿应用，以对其发展提供全面的见解。从空间数据、时间数据和数据生成三个应用场景的角度，介绍了对应的三种深度学习模型类型，分别是卷积神经网络（CNN）、循环神经网络（RNN）和生成对抗网络（GAN），以及他们的主要应用任务。此外，本研究还讨论了深度学习在海洋学领域当前的瓶颈和未来的创新前景。 通过对现有研究的总结和分析，我们的目的是深入探讨深度学习在物理海洋学中的潜力和挑战，为未来海洋学研究的研究者提供参考和启发。