Leveraging satellite observations and machine learning for underwater sound speed estimation

作者：Madusanka Madiligama, Zheguang Zou, Likun Zhang · 发表于：Communications Engineering · 年份：2025 · DOI：10.1038/s44172-025-00459-6 · 被引用次数：2 · 研究领域：Underwater Acoustics Research、Underwater Vehicles and Communication Systems、Arctic and Antarctic ice dynamics

Underwater acoustics plays a vital role in climate science, marine ecosystems, environmental monitoring, mineral exploration, and oceanography. Accurate underwater sound speed data is crucial for acoustic modeling and applications such as sonar systems. However, limited data and computational constraints hinder real-time, high-resolution mapping of three-dimensional sound speed fields. We present an integrated approach that combines remote sensing, machine learning, and underwater acoustics to estimate sound speed across vast ocean regions. By analyzing sea surface temperature and salinity from satellite observations, we use machine learning to rapidly and accurately predict 3D underwater sound speed. Incorporating spatial and temporal variables enables detailed, real-time mapping. Validation against in-situ profiles and Argo float data confirms the model’s accuracy across seasons, regions, and timeframes. This approach advances underwater sound speed prediction beyond traditional limits. Acoustic propagation modeling further demonstrates the potential of our model for applications in underwater detection, communication, and noise analysis. Underwater sound speed is essential for sonar and ocean exploration, but real-time 3D mapping is limited by sparse data and high computational demands. Madiligama, Zou, and Zhang leverage machine learning and satellite data to enable fast and accurate oceanic sound speed predictions.