一种基于抛物线方程的基于物理的机器学习方法,用于水下声音传播模型.
Ziwei Huang1, Liang An1, Yang Ye1
1Key Laboratory of Underwater Acoustic Signal Processing (Southeast University), Ministry of Education, Nanjing, 210096, China.
The Journal of the Acoustical Society of America
|January 29, 2026
概括
U-PARANET是一个新的基于物理学的神经网络,通过整合抛物线方程,准确地模拟了远程水下声音传播. 这种方法提高了稳定性,并减少了海洋监测和通信应用程序的错误.
科学领域:
- 海洋声学 海洋声学
- 水下物理学的研究.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 传统的水下声音传播模型面临着计算和适应性限制.
- 数据驱动的方法缺乏物理约束,需要大量的数据集.
- 基于物理学的神经网络在准确的远程推断方面扎.
研究的目的:
- 开发一种基于物理的机器学习方法,用于准确的远程水下声音传播建模.
- 为了提高稳定性并减少声学传播预测中的错误积累.
- 改善水下环境监测,通信和定位.
主要方法:
- 提出U-PARANET,一个基于物理的机器学习模型.
- 在神经网络架构中将抛物线方程纳入硬约束.
- 利用抛物线方程的递归结构来提高稳定性.
主要成果:
- 在模拟和实验数据中,U-PARANET准确地预测了传输损失和相位结构.
- 实现了传输损失的低平均绝对误差:1.40dB (理想),1.06dB (模拟的SWellEx-96) 和2.87dB (实验的SWellEx-96).
- 在空间场模式和强大的长距离外推方面表现出良好的一致性.
结论:
- U-PARANET 展现出优秀的远程水下声音传播建模能力.
- 该方法在具有挑战性的,现实的海洋环境中显示出强大的外推.
- U-PARANET为海洋学应用提供了一个有前途的进步.
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