Read Ashkezari

[rabernat]

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016GL071269

We report a novel altimetry‐based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

There could be some good ideas in here in terms of ML architecture, but IMO the fundamental premise is flawed:

A balanced set of eddy and noneddy structures were manually identified for training and validation purposes.