利用声速动态和生成深度学习来实现基于光线的海洋声学断层扫描
Priyabrata Saha1, Richard X Touret2,3, Etienne Ollivier3
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
JASA express letters
|April 1, 2025
概括
一个新的深度学习框架改进了海洋声学断层扫描 (OAT) 用于估计声速概况 (SSP). 这种方法比水下声学传感的传统技术提供了更高的准确性.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学方面
- 机器学习 机器学习
- 地质物理学 地质物理学
背景情况:
- 海洋声学断层扫描 (OAT) 是估计声速概况 (SSP) 的一个反向问题.
- 传统的OAT方法往往面临着不合理的挑战,限制了SSP估计的准确性.
- 准确的SSP对于水下航行,通信和海洋研究至关重要.
研究的目的:
- 为基于光线的OAT引入一个生成的深度学习框架.
- 提高海洋环境中SSP估计的准确性和稳定性.
- 为了克服传统OAT配方的不良性质.
主要方法:
- 采用了一个生成的深度学习框架,包含一个变量自动编码器.
- 采用线性动态模型来进行SSP变化的强大的低维参数化.
- 从区域海洋模型和各种传感器设置中模拟SSP变化的框架进行了测试.
主要成果:
- 深度学习框架显著优于传统的线性最小方程OAT配方.
- 在不同的海洋条件和传感器配置下,在估计SSP方面表现强.
- 通过使用先进的机器学习规范化,成功解决了OAT的不良性质.
结论:
- 拟议的生成深度学习框架为海洋声学断层扫描提供了强大的进步.
- 这种方法提供了更准确,更可靠的声速概况估计.
- 这些发现对改善水下声学传感和海洋学研究具有重大意义.
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