Learning-based estimation of in-situ wind speed from underwater acoustics

TL;DR

This paper presents a deep learning method that estimates sea surface wind speed from underwater acoustic data, outperforming existing data-driven approaches and leveraging multimodal data for improved accuracy and robustness.

Contribution

It introduces a novel deep learning framework that combines physical knowledge with data-driven modeling for wind speed estimation from underwater acoustics.

Findings

Outperforms state-of-the-art methods with up to 16% RMSE reduction.

Utilizes underwater acoustic data's temporal dynamics to improve wind speed tracking.

Combines underwater acoustics with reanalysis data to enhance robustness and accuracy.

Abstract

Wind speed retrieval at 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 become more and more appealing to fully exploit the potential of observation datasets. 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 to benefit both from 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 up to 16% in terms of 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 ECMWF reanalysis data, may further improve the reconstruction performance, including the robustness with respect to missing underwater acoustics data.