Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations
Machine Learning
2020-05-20 v1 Machine Learning
Signal Processing
Applied Physics
Abstract
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.
Keywords
Cite
@article{arxiv.2003.10661,
title = {Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations},
author = {Xiaolei Li and Wenhua Song and Dazhi Gao and Wei Gao and Haozhong Wan},
journal= {arXiv preprint arXiv:2003.10661},
year = {2020}
}