Madiligama Madusanka, Zou Zheguang, Zhang Likun
National Center for Physical Acoustics and Department of Physics and Astronomy, University of Mississippi, University, MS, USA.
Commun Eng. 2025 Jul 15;4(1):126. doi: 10.1038/s44172-025-00459-6.
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.
水下声学在气候科学、海洋生态系统、环境监测、矿物勘探和海洋学中发挥着至关重要的作用。准确的水下声速数据对于声学建模和诸如声纳系统等应用至关重要。然而，数据有限和计算限制阻碍了三维声速场的实时、高分辨率测绘。我们提出了一种综合方法，将遥感、机器学习和水下声学结合起来，以估计广阔海洋区域的声速。通过分析卫星观测得到的海面温度和盐度，我们利用机器学习快速准确地预测三维水下声速。纳入空间和时间变量能够实现详细的实时测绘。与现场剖面和阿尔戈浮标数据进行的验证证实了该模型在不同季节、区域和时间范围内的准确性。这种方法超越了传统限制，推进了水下声速预测。声学传播建模进一步证明了我们的模型在水下探测、通信和噪声分析应用中的潜力。
