Oil Spill Drift Prediction Enhanced by Correcting Numerically Forecasted Sea Surface Dynamic Fields With Adversarial Temporal Convolutional Networks

Timely and accurate representation of sea surface dynamic fields is crucial for oil spill drift prediction. Numerically forecasted sea surface dynamic fields are available in a timely manner, but their accuracy is limited. Conversely, reanalysis sea surface dynamic fields offer superior accuracy but suffer from time delays. To enhance the performance of oil spill drift prediction, we propose a deep-learning-based approach to correcting numerically forecasted sea surface dynamic fields, aligning them more closely with reanalysis sea surface dynamic fields. Our approach introduces an adversarial temporal convolutional network (ATCN) framework, consisting of a temporal convolutional network (TCN)-based corrector and a discriminator. The TCN can characterize sea surface dynamic field sequences both spatially and temporally. In this scenario, the corrector processes the numerically forecasted sea surface dynamic fields and outputs corrected sea surface dynamic fields that approximate the reanalysis sea surface dynamic fields. Adversarial training with the discriminator further refines the corrector. This approach enhances timely oil spill drift prediction using the corrected sea surface dynamic fields. We also provide a dataset of oil spill drifts from the Symphony and Sanchi accidents, including related sea surface dynamic field data and oil spill remote sensing data, establishing a baseline for evaluating oil spill drift prediction. Experiments on this dataset validate the ATCN framework’s effectiveness in enhancing oil spill drift prediction.

IEEE Transactions on Geoscience and Remote Sensing