从端到端的水下声传输损失预测与自适应的多尺度扩展网络
Zhao Sun1, Yongxian Wang1, Wei Liu1
1College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.
The Journal of the Acoustical Society of America
|January 21, 2025
概括
这项研究引入了一种新的深度学习模型,MultiScale-DUNet,用于预测水下声传输损失 (TL). 数据驱动的方法在复杂的海洋环境中提供了快速而准确的声场预测.
科学领域:
- 海洋声学 海洋声学
- 深度学习应用程序深度学习应用程序
- 计算物理学的计算物理.
背景情况:
- 水下声学传播是复杂的,传统上用计算密集的微分方程来解决.
- 现有的水下声学深度学习研究主要集中在反向问题上,使得前向建模的诞生.
- 准确的前建模对于诸如水下勘探和监测等应用至关重要.
研究的目的:
- 提出和评估一个端到端的深度学习架构,用于预测水下声传输损失 (TL).
- 开发一个基于数据的模型,能够快速准确地预测完整的声场.
- 解决关于水下声学传播的先进物理建模研究中的差距.
主要方法:
- 开发了一个端到端的深度学习架构,MultiScale-DUNet.
- 该架构集成了一个U-Net模型与一个自适应的多尺度扩展模块.
- 该模型将多尺度的声场信息同化为预测.
主要成果:
- 多尺度DUNet在一个端到端的框架内成功预测了复杂的2D海洋环境中的声学TL.
- 该模型展示了快速预测能力,同时保持高准确度.
- 预测是在计算上便宜的条件下实现的.
结论:
- 开发的MultiScale-DUNet提供了一种高效准确的方法来预测水下声学TL.
- 与传统方法相比,这种数据驱动的方法显著减少了计算时间和成本.
- 该技术在实时水下勘探和监测方面具有广泛的应用.
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