基于随机模式耦合矩阵模型训练U网恢复声干扰条纹
Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations
摘要：
训练U-Net从失真的条纹中恢复声干扰条纹（AIS）。引入随机模式耦合矩阵模型，快速生成大量训练数据，用于训练U-网络。在具有非线性内波的距离相关波导中测试了U-Net的AIS恢复性能。虽然随机模式耦合矩阵模型不是一个精确的物理模型，但测试结果表明，U-Net成功地恢复了不同信噪比（SNR）和不同形状的NLIW的不同振幅和宽度下的AIS。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
