通过深度学习预测SOFAR通道中的水下声传输损失从射线轨迹
Haitao Wang1, Shiwei Peng1, Qunyi He1
1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, Chinawht@nwpu.edu.cn, pengsw@mail.nwpu.edu.cn, hequnyi123@mail.nwpu.edu.cn, zenggxy@nwpu.edu.cn.
JASA express letters
|May 8, 2024
概括
一种新的深度学习方法准确地预测了SOFAR通道中的水下声传输损失. 这种方法为复杂的古典方法提供了快速可靠的替代方案,用于声学建模.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学上
- 机器学习 机器学习
背景情况:
- 预测声传输损失的古典方法在声音定位和范围 (SOFAR) 频道是计算密集和复杂的.
- 准确的声学传播建模对于水下应用至关重要.
研究的目的:
- 开发一种计算效率高,准确的方法,用于预测SOFAR通道中的声传输损失.
- 为了利用深度学习进行水下声学建模.
主要方法:
- 训练了一种U-net类型的卷积神经网络,以映射射线轨迹到传输损失.
- 该模型在SOFAR通道模拟中使用Munk的声速配置文件进行了验证.
主要成果:
- 深度学习模型展示了射线轨迹和传输损失之间的准确映射.
- 提出的方法显示了作为快速预测模型的潜力.
结论:
- 深度学习为传统声传输损失预测的计算挑战提供了可行的解决方案.
- 基于U-net的方法提供了准确的水下声学建模,而不会牺牲性能.
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