Detecting Internal Waves From Altimeter Data Using Deep Learning Method

The widespread presence of oceanic internal waves (IWs) across continental shelves, straits, and islands has been confirmed using multiple satellite payloads, including optical and synthetic aperture radar (SAR) sensors. However, the efficiency and accuracy of IWs detection are severely limited by the cloud contamination of optical images and the availability of SAR data. In other words, although IWs can be observed by multiple sensors, achieving full-time coverage remains challenging. The SAR altimeter (SRAL), characterized by high spatial resolution and continuous observation capability, holds substantial potential for IWs detection. Consequently, this study proposes a deep-learning-based method, named the IWs detection network (IWD-Net), to detect IWs from SRAL data. The IWD-Net is trained and tested in the Andaman Sea, achieving a detection precision of 96.2%. In addition, the model is subsequently applied to the South China Sea (SCS), where the detection precision of 94.9% reconfirms its robustness and reliability in detecting IWs. Statistical results indicate that the IWs detection efficiency using altimeter data improves by 227% compared to SAR and optical sensors combined. Finally, spatiotemporal analysis reveals that IWs are primarily concentrated in the western Luzon Strait and the Sulu Sea, but the seasonal variations of IWs in these two regions exhibit opposite trends: IWs are more active in summer/autumn within the western Luzon Strait, whereas IWs are more active in late winter/early spring within the Sulu Sea. These findings highlight the potential of altimeter data to fill gaps in IWs observations and enhance our understanding of ocean dynamics.