Eddy-SPIN: A Spatial Projection Inference Network for Oceanic Mesoscale Eddy Trajectory Prediction

Efficient oceanic mesoscale eddy (ME) trajectory prediction can help us to better understand energy transport in the ocean while also ensuring maritime safety. However, the complex, nonlinear motion of MEs presents significant challenges. Existing data-driven methods are primarily focused on single-trajectory prediction, ignoring interactions between multiple MEs, which are the most important factors influencing trajectory evolution. This study addresses two major research limitations: a lack of methodologies that explicitly include multi-ME interactions, as well as a lack of quantitative assessments of their impact on prediction accuracy. To fill these gaps, this study proposes a spatial projection inference network for oceanic ME trajectory prediction (Eddy-SPIN). Eddy-SPIN uses a specially designed projection method based on ME intrinsic features to transform multiple 1-D time-series trajectory data into 2-D Gaussian distribution maps. Then, a spatiotemporal prediction network is designed to generate future Gaussian maps, which are then inverse projected onto trajectories. Extensive evaluations using data from South China Sea (SCS) show that Eddy-SPIN reduces the seven-day cumulative mean geodesic distance (MGD) error by at least 31.9% and 53.3% when compared with the state-of-the-art single-trajectory method and numerical model, respectively. Furthermore, additional experiments conducted across four dynamically distinct oceanic regions consistently confirm the generalizability and robustness of Eddy-SPIN. The proposed Eddy-SPIN verifies the importance of interactions in the ME trajectory prediction problem and presents a novel predictive model that differs from previous works.