Yoon Seunghyun, Park Yongsung, Gerstoft Peter, Seong Woojae
Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2024 Mar 1;155(3):2037-2049. doi: 10.1121/10.0025235.
Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases, even at frequencies below 100 Hz, posing a challenge for neural networks to converge to accurate solutions. To address this, we utilize the envelope function from the parabolic-equation technique, fundamental in ocean sound propagation modeling. The envelope function shows slower variations across ranges, enabling PINNs to predict sound pressure in an ocean waveguide more effectively. Additional PDE information allows PINNs to capture PDE solutions even with a limited amount of training data, distinguishing them from purely data-driven machine learning approaches that require extensive datasets. Our approach is validated through simulations and using data from the SWellEx-96 experiment.
本文提出了一种基于不同距离深度处部分测量压力幅值的海洋声压场预测方法。我们提出的机器学习策略采用了一个经过训练的神经网络，以距离深度作为输入，并输出该位置的复声压。我们利用了一种物理信息神经网络（PINN），在拟合采样数据的同时考虑了控制海洋声压场的偏微分方程（PDE）提供的额外信息。在千米级距离的广阔海洋环境中，即使在低于100 Hz的频率下，压力场的相位也会快速波动，这给神经网络收敛到精确解带来了挑战。为了解决这个问题，我们利用了抛物线方程技术中的包络函数，这是海洋声传播建模的基础。包络函数在距离上的变化较慢，使PINN能够更有效地预测海洋波导中的声压。额外的PDE信息使PINN即使在训练数据有限的情况下也能捕捉PDE解，这使它们有别于需要大量数据集的纯数据驱动机器学习方法。我们的方法通过模拟以及使用SWellEx - 96实验的数据进行了验证。
