宽带模态相位速度估计和地声逆转与稀疏的贝叶斯学习使用多范围数据a)
Shanru Lin1,2, Haiqiang Niu1,2, Zhenglin Li3,4
1State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
The Journal of the Acoustical Society of America
|September 24, 2025
概括
一种新的多范围稀疏贝叶斯学习 (SBL) 方法增强了浅水中的地球声学反转. 这种技术准确地估计了宽带模态相位速度,改善了水下声学定位.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学方面
- 信号处理 信号处理
背景情况:
- 浅水中的地球声学倒置对于水下声学至关重要.
- 传统方法面临诸如窄带限制和参数干扰等局限性.
研究的目的:
- 提出一种使用多范围稀疏贝叶斯学习 (多范围SBL) 的宽带模态相位速度估计方法.
- 为了提高地声逆转的准确性和在浅水环境中的定位.
主要方法:
- 使用垂直线阵列的多范围信号,用于本地模态深度函数和水平波数.
- 使用稀疏贝叶斯学习 (SBL) 来克服窄带限制和解海底衰减.
- 应用贝叶斯优化来高效的多维参数估计.
主要成果:
- 准确估计横向波数在宽带宽的水平波数.
- 通过调整宽带相位速度曲线,成功地实现了地声逆转.
- 海底衰减系数的脱估计,减少相互干扰.
- 与传统方法相比,改进了范围和深度估计.
结论:
- 多范围SBL方法为浅水地声逆转提供了强大而准确的方法.
- 该方法能够处理局部模式并减少参数干扰的能力提高了反转可靠性.
- 通过模拟和实验数据验证,在本地化方面表现出卓越的性能.
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