Learning-Based Temporal Estimation of In-Situ Wind Speed From Underwater Passive Acoustics

Wind speed retrieval at the sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea-surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information. Whereas model-driven schemes, especially data assimilation approaches, are the state-of-the-art schemes to address inverse problems in geoscience, machine learning techniques have become more and more appealing to fully exploit the potential of observation data sets. Here, we introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics possibly complemented by other data sources such as weather model reanalyses. Our approach bridges data assimilation and learning-based frameworks that benefit from both prior physical knowledge and computational efficiency. Numerical experiments on real data demonstrate that we outperform the state-of-the-art data-driven methods with a relative gain of up to 16% in terms of root-mean-squared error (RMSE). Interestingly, these results support the relevance of the time dynamics of underwater acoustic data to better inform the time evolution of wind speed. They also show that multimodal data, here underwater acoustics data combined with European Center of Medium-Range Weather Forecast (ECMWF) reanalysis data, may further improve the reconstruction performance, including the robustness with respect to missing underwater acoustics data.