找到正确的平衡:使用张量神经网络进行三维海洋声速场重建
Siyuan Li1, Lei Cheng1, Ting Zhang1
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
The Journal of the Acoustical Society of America
|August 22, 2023
概括
这项研究介绍了一种新的张量深度神经网络 (DNN),用于3D海洋声速场重建. 与现有技术相比,拟议的方法显著提高了准确性和噪声排斥.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学方面
- 数据科学数据科学数据科学
背景情况:
- 精确的3D海洋声速场 (SSF) 重建对于海洋声学至关重要.
- 由于数据稀疏和不确定的原因,现有的方法,如线条插值,张量完成和深度神经网络 (DNN) 面临挑战.
- 缺乏原则性分析阻碍了选择最佳的3DSSF重建技术.
研究的目的:
- 分析各种3DSSF重建方法的有效性.
- 为3DSSF重建提出一种新的,平衡的表示模型.
- 开发一个 3D SSF 量身定制的张量 DNN,集成表达力和简洁性.
主要方法:
- 对现有3DSSF方法的重建错误进行了彻底的分析.
- 开发了一个 3D SSF 量身定制的张量 DNN 集成张量计算和 DNN 架构.
- 拟议的模型概括了以前基于张数的SSF表示模型,并结合了噪声拒绝功能.
主要成果:
- 拟议的张量DNN实现了显着的3DSSF重建.
- 使用南中国海数据的数值结果显示,其性能优于最先进的方法.
- 该方法显示了自然拒绝噪声的能力.
结论:
- 开发的3D SSF量身定制的张量 DNN 为海洋声速场重建提供了一种优越的方法.
- 这种方法通过提供平衡和可靠的表示来解决现有技术的局限性.
- 这些发现通过在现实世界海洋学数据中的显著性能改进来验证.
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