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A U-Net is trained to recover acoustic interference striations (AISs) fromdistorted ones. A random mode-coupling matrix model is introduced to generate alarge number of training data quickly, which are used to train the U-Net. Theperformance of AIS recovery of the U-Net is tested in range-dependentwaveguides with nonlinear internal waves (NLIWs). Although the randommode-coupling matrix model is not an accurate physical model, the test resultsshow that the U-Net successfully recovers AISs under different signal-to-noiseratios (SNRs) and different amplitudes and widths of NLIWs for differentshapes.

Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations