一种使用卫星高度计数据且基于密度聚类的新型海洋中尺度涡自动探测算法

中尺度涡旋是海洋中典型的中尺度现象，是海洋中能量传递的运输者，中尺度涡识别与提取是物理海洋学研究的重要内容之一，而中尺度涡自动发现算法是最基础的用于寻找与分析中尺度涡的工具。中尺度涡旋探测工作的数据来源主要为卫星高度计数据融合出的SLA数据，该数据可以客观的描述海洋表层高度状态。中尺度涡表示为SLA闭合等值线所包围的局部等值区域，涡旋识别需要从SLA数据中提取出稳定的闭合等值线结构。针对基于SLA数据中的中尺度涡探测的特点，本文提出了一种新的基于聚类方法的中尺度涡自动识别算法，通过对SLA数据集的分割与筛选将中尺度涡区域与背景区域分离，后建立区域内联系并将其映射到SLA地图上来提取中尺度涡结构。本文算法解决了传统探测算法中参数设定的敏感性问题，不需要进行稳定性测试，算法适应性增强。算法中加入了涡旋筛选机制，保证了结果的涡旋结构的稳定性，提高了识别准确率。在此基础上，本文选取了西北太平洋及中国南海地区进行了中尺度涡探测实验，实验结果展示出了本文算法在较传统算法提高算法效率的同时，也保持着较高的算法稳定性，可以在稳定识别各个单涡结构的同时识别稳定的多涡结构。

A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering

The mesoscale eddy is a typical mesoscale oceanic phenomenon that transfers ocean energy. The detection and extraction of mesoscale eddies is an important aspect of physical oceanography, and automatic mesoscale eddy detection algorithms are the most fundamental tools for detecting and analyzing mesoscale eddies. The main data used in mesoscale eddy detection are sea level anomaly (SLA) data merged by multi-satellite altimeters'' data. These data objectively describe the state of the sea surface height. The mesoscale eddy can be represented by a local equivalent region surrounded by an SLA closed contour, and the detection process requires the extraction of a stable closed contour structure from SLA maps. In consideration of the characteristics of mesoscale eddy detection based on SLA data, this paper proposes a new automatic mesoscale eddy detection algorithm based on clustering. The mesoscale eddy structure can be extracted by separating and filtering SLA data sets to separate a mesoscale eddy region and non-eddy region and then establishing relationships among eddy regions and mapping them on SLA maps. This paper overcomes the problem of the sensitivity of parameter setting that affects the traditional detection algorithm and does not require a sensitivity test. The proposed algorithm is thus more adaptable. An eddy discrimination mechanism is added to the algorithm to ensure the stability of the detected eddy structure and to improve the detection accuracy. On this basis, the paper selects the Northwest Pacific Ocean and the South China Sea to carry out a mesoscale eddy detection experiment. Experimental results show that the proposed algorithm is more efficient than the traditional algorithm and the results of the algorithm remain stable. The proposed algorithm detects not only stable single-core eddies but also stable multi-core eddy structures.