Vertical Structure-Based Classification of Oceanic Eddy Using 3-D Convolutional Neural Network

The eddy identification is an important part of human cognition of the ocean. Significant achievements have been made by using sea level anomaly (SLA) data observed by the altimeter. However, the abundant eddies, which do not cause sea surface characteristic anomalies, cannot be identified. In this study, the eddy subsurface vertical structure-oriented 3-D neural network is developed to classify the oceanic eddies. This study is among the first that explores the ability of deep learning in eddy identification with vertical structure. First, the purified eddy profiles dataset is constructed based on the fact that the structure derived from vertical profiles is highly correlated with the sea surface topography detected by altimetry. Then, the eddy vertical structure-oriented 3-D neural network based on the residual network (ResNet) is constructed, which can classify the eddies as anticyclonic eddies (AEs), cyclonic eddies (CEs), and noneddies (NEs) effectively. Furthermore, the spatial and temporal features can be combined in the proposed network as external factors. Meanwhile, through 3-D convolutions and 3-D pooling, the proposed network is capable of modeling 3-D eddy data and can be extended to the deeper network structure. Finally, the classification experiments are implemented to validate the performance of the proposed methodology. The most striking result emerging from experiments is that the proposed method can expand the capacity of eddy identification by using vertical profiles as calibrated by altimetry with competitive classification performance. Together these results provide important insights into the application of artificial intelligence in oceanic eddy research.