基于物理学的神经网络支持模态波数估计
Seunghyun Yoon1, Yongsung Park2, Keunhwa Lee3
1Institute of Engineering Research, Seoul National University, Seoul 08826, Republic of Korea.
The Journal of the Acoustical Society of America
|October 9, 2024
概括
基于物理学的神经网络 (PINNs) 改善了海洋声学模态波数估计. 海洋PINN完善了空间不连贯的数据,通过稀疏的贝叶斯学习提高了准确性,以更好地分析海洋声音传播.
科学领域:
- 海洋声学 海洋声学
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 估计水平模态波数对于理解海洋声学传播至关重要.
- 由于环境因素,海洋压力数据往往存在空间不一致,阻碍了准确的波数估计.
- 传统的方法在声学数据中的范围连贯性下降,而传统的方法在声学数据中的范围连贯性下降.
研究的目的:
- 介绍OceanPINN,一个基于物理的神经网络,用于管理空间不连贯的海洋声学数据.
- 通过提高数据范围的一致性,提高模态波数估计的准确性.
- 用模拟和实验海洋声学数据验证拟议的方法.
主要方法:
- 使用汉克尔变换将取决于范围的压力场与波数域联系起来.
- 采用在数据大小上训练的物理信息神经网络 (OceanPINN) 来预测阶段精制数据.
- 将模态波数估计技术应用于精细的,范围一致的数据.
- 整合稀疏的贝叶斯学习来进行高分辨率波数估计.
主要成果:
- 海洋PINN通过预测阶段精制信号,有效地管理空间不连贯的海洋声学数据.
- 精细数据的增强范围连贯性导致模态波数估计准确度显著提高.
- 稀有贝叶斯式学习进一步细化了估计的模态波数的准确性.
- 该方法在模拟数据集和真实世界SWellEx-96实验数据上都表现出有效性.
结论:
- 基于物理学的神经网络,特别是OceanPINN,提供了一个强大的解决方案,可以从具有挑战性的海洋声学数据中改进模态波数估计.
- 拟议的方法通过解决空间非连贯性来提高数据质量,从而导致更可靠的声学传播模型.
- 将OceanPINN与稀疏的贝叶斯学习相结合,为高级海洋声信号处理和分析提供了一个强大的工具.
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