通过神经网络在海洋波导中估计模态波数和组速度
1Université Grenoble Alpes, CNRS, Grenoble-INP, GIPSA-Lab, 38000 Grenoble, France.
JASA express letters
|June 12, 2023
概括
深度神经网络有效地预测水下声学传播参数,降低宽带信号的计算成本. 这种方法可以增强正常模式的模拟,而不会牺牲准确度.
科学领域:
- 声学 声学 在声学上
- 机器学习 机器学习
- 海洋学 海洋学 海洋学
背景情况:
- 水下声学传播受到水和海底特性的影响.
- 对于宽带信号的正常模式模拟是计算密集的.
研究的目的:
- 开发一种计算效率高的方法来建模水下声学传播.
- 为了降低宽带信号正常模式模拟的计算成本.
主要方法:
- 使用深度神经网络 (DNN) 来预测模态水平波数和组速度.
- 预测的波数被用来计算模态深度函数和传输损失.
主要成果:
- DNN 方法显著降低了计算成本.
- 与传统方法相比,精度保持不变,没有明显的损失.
- 该方法在模拟的浅水2006逆转场景上进行了演示.
结论:
- 深度神经网络为模拟水下声学传播提供了一个有效的替代方案.
- 这种方法可以应用于复杂的场景,如浅水环境.
- DNN方法为减少声传播建模中的计算负载提供了可行的解决方案.
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