基于机器学习的海洋中尺度涡检测识别研究综述  

Overview on ocean mesoscale eddy detection and identification based on machine learning

作　　者：张家灏 邓科峰[2] 聂腾飞 任开军[2] 宋君强[2] ZHANG Jia-hao;DENG Ke-feng;NIE Teng-fei;REN Kai-jun;SONG Jun-qiang(College of Computer Science and Technology,National University of Defense Technology,Changsha 410073;College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学计算机学院,湖南长沙410073 [2]国防科技大学气象海洋学院,湖南长沙410073

出　　处：《计算机工程与科学》2021年第12期2115-2125,共11页Computer Engineering & Science

摘　　要：海洋中尺度涡是一种重要的海洋中尺度现象,在海洋环流、物质能量传输中发挥重要作用,对舰船航行安全、水声通信等也具有重要的影响。高效准确地检测识别出海洋中尺度涡无论对于物理海洋认知还是海洋开发利用都有着重要的研究价值。传统涡旋检测识别方法依赖专家经验设计的单一阈值,具有显著的主观性。随着深度学习的兴起,机器学习方法在涡旋检测识别的准确性和自动化程度上表现出一定的优势。通过总结与对比分析现有基于机器学习的检测识别方法,为发展海洋中尺度涡检测识别的研究提供系统认知和参考依据。Ocean mesoscale eddy is an important ocean mesoscale phenomenon that plays an important role in ocean circulation,material and energy transport,and has an important impact on the safety of ship navigation and hydroacoustic communication.Efficient and accurate detection and identification of ocean mesoscale eddies are of great research value for both physical ocean cognition and ocean exploitation.Traditional eddy detection and identification methods rely on a single threshold designed by experts'experiences.With the rise of deep learning,the current machine learning methods show certain advantages in the accuracy and automation of eddy detection and identification.This paper summarizes and comparatively analyzes the existing machine learning-based detection and identification methods to provide a systematic knowledge and reference basis for the development of ocean mesoscale eddy detection and identification research.

关 键 词：中尺度涡 人工智能 机器学习 深度学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]