  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.
