海洋涡旋智能检测研究进展

Recent developments in AI-based oceanic eddy identification

海洋涡旋是一种常见的海洋现象,在全球海洋物质和能量的输运中起着重要作用。随着海洋研究技术手段的不断提升,各类海洋涡旋检测方法应运而生。传统涡旋检测方法应用广泛,但其过度依赖于专家经验设置阈值和持续的人工干预,存在检测误差较大、工作效率低以及全球普适性差等问题,难以适应复杂多变的海洋环境。当前人工智能快速发展,其在海洋涡旋智能检测中能够自动、快速地提取图像深层特征,有效解决海洋现象特征相似度高、几何差异大的问题。该文立足于当前海洋涡旋智能检测的发展现状,从编码器-解码器结构、全卷积神经网络、多尺度上下文方法和注意力机制等方面回顾了不同深度学习方法在海洋涡旋智能检测中的应用,以期为海洋涡旋研究提供一些启示和参考。

Ocean eddies are prevalent oceanic phenomenon that play a crucial role in the global transportation of oceanic materials and energy.Although traditional methods for detecting ocean eddies are widely used,they suffer from significant drawbacks such as excessive reliance on expert-set thresholds,continuous manual intervention,large detection errors,low efficiency,and poor global applicability,making it difficult to adapt to the complex and variable marine environment.Currently,the rapid development of artificial intelligence(AI)presents a promising solution for the intelligent detection of ocean eddies.AI can automatically and rapidly extract deep features from images,effectively address the challenges posed by the high similarity in oceanic phenomenon features and significant geometric variability.This paper provides an overview of AI-based oceanic eddy identification methods based on different deep learning methods,focuses on coder-decoder structure,fully convolutional neural network,multi-scale context method and attention mechanism,and aims to provide valuable insights and references for future ocean eddy research.