Mesoscale Eddy Detection Based on the Deep Learning Method

Mesoscale eddies play an essential role in ocean momentum, energy, heat, and mass transport. Thus, it can also significantly affect the distribution of nutrients, phytoplankton, as well as the shoal of fish. In order to quickly and accurately locate the mesoscale eddies in the ocean, we fused the velocity, divergence, vorticity, and Okubo-Weiss (OW) parameter information with the sea surface height anomalies (SSHA) data, respectively. Then, the feature pyramid network (FPN) in the deep learning method was adopted to extract the eddy position from fused information. The experiment shows that the proposed method can effectively detect the mesoscale eddy locations. Besides, it can also be concluded that, compared with the merged data with velocity and divergence, the FPN performs better on fused data with vorticity and OW information.