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基于物理的机器学习用于匹配的场源范围估计)
1Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02540, USA.
The Journal of the Acoustical Society of America
|December 12, 2025
概括
一个新的基于物理的机器学习框架,使用匹配的现场处理,准确地定位水下声音源. 这种方法通过将物理整合到AI模型中,在数据有限的场景中增强了海洋声学定位.
科学领域:
- 海洋声学 海洋声学
- 机器学习是机器学习.
- 信号处理 信号处理
背景情况:
- 海洋声源的定位对于水下监测和研究至关重要.
- 传统的匹配场处理 (MFP) 方法通常需要大量的环境数据,并且对不匹配很敏感.
- 纯粹基于数据的机器学习 (ML) 方法可能缺乏物理一致性.
研究的目的:
- 为海洋声源定位开发基于物理的机器学习 (ML) 框架.
- 将物理信息神经网络 (PINNs) 集成到匹配场处理 (MFP) 方案中.
- 为了实现精确的源接收器范围估计,采用稀疏测量和减少环境特征.
主要方法:
- 使用物理信息神经网络 (PINN) 来从稀疏测量和已知的声速概况 (SSP) 中预测声压场.
- PINN预测的复制字段被集成到MFP算法中.
- 该框架使用1996年浅水评估细胞实验 (SWellEx-96) 的实验数据进行了验证.
主要成果:
- 拟议的方法实现了精确的源接收器范围估计,即使在最接近点等具有挑战性的场景中也是如此.
- 该框架证明了对稀疏阵列配置和中等声速概况 (SSP) 不匹配的稳定性.
- 在训练期间排除的阵列元素深度的性能保持不变,显示出良好的插值/外推能力.
结论:
- 基于物理学的ML提供了一种强大的方法,用于在现实的,数据有限的环境中定位海洋声学.
- 这种方法克服了传统基于模型的MFP的局限性,通过减少对环境的依赖和减轻不匹配效应.
- 将物理整合到机器学习模型中可以产生物理一致的预测,提高本地化准确性和通用性.
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