论文标题
基于随机模式耦合矩阵模型训练U-NET，以恢复声学干扰条纹
Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations
论文作者
论文摘要
对U-NET进行了训练，可以从扭曲的声带中恢复声学干扰条纹（AISS）。引入了随机模式耦合矩阵模型，以快速生成大量训练数据，这些数据用于训练U-NET。用非线性内波（NLIWS）在范围依赖性波导中测试了U-NET的AIS回收的性能。尽管随机模式耦合矩阵模型不是一个准确的物理模型，但测试结果表明，U-NET在不同的信噪比（SNRS）下成功恢复了AISS，而不同形状的NLIWS的不同振幅和宽度也是如此。
A U-Net is trained to recover acoustic interference striations (AISs) from distorted ones. A random mode-coupling matrix model is introduced to generate a large number of training data quickly, which are used to train the U-Net. The performance of AIS recovery of the U-Net is tested in range-dependent waveguides with nonlinear internal waves (NLIWs). Although the random mode-coupling matrix model is not an accurate physical model, the test results show that the U-Net successfully recovers AISs under different signal-to-noise ratios (SNRs) and different amplitudes and widths of NLIWs for different shapes.
