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Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations
Xiaolei Li, Wenhua Song, Dazhi Gao, Wei Gao, Haozhong Wan · 2020-03-24 · via stat.ML updates on arXiv.org

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.

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