Bridging gaps in ocean current modelling: a hybrid physics-driven deep learning approach

Global Ocean Surface Currents (GOSC) play a critical role in understanding ocean dynamics and tracking pollutant dispersion, such as marine microplastics. However, accurately reconstructing GOSC remains a challenge. To address this, we propose PhyGOSCR-Net, a physics-driven deep learning model that employs convolutional layers to extract multiscale features and attention mechanisms to maintain global coherence. We train the model using geostrophic currents, Ekman currents, and Stokes drift as inputs, with Argo float velocities at 0 m and drifter-derived velocities at 15 m as labels, to reconstruct physics-driven GOSC at both depths. Experimental results show that our model outperforms GLOBCURRENT, particularly in equatorial regions where geostrophic assumptions fail, reducing Root Mean Square Error by 10–30 cm/s across both zonal and meridional components. The high correlation with observational data (correlation coefficient > 0.8) confirms its ability to model coupled physical processes. Based on reconstructed ocean currents, microplastic transport simulations successfully identify the five major global accumulation zones, including the Great Pacific Garbage Patch, demonstrating their strong potential for environmental research. This work advances high-precision ocean current reconstruction and supports pollution tracking and mitigation efforts.