Convolutional recurrent autoencoder network for learning underwater ocean acoustics

Underwater ocean acoustics is a complex physical phenomenon involving not only widely varying physical parameters and dynamical scales but also uncertainties in the ocean parameters. Thus, it is difficult to construct generalized physical models which can work in a broad range of situations. In this regard, we propose a convolutional recurrent autoencoder network (CRAN) architecture, which is a data-driven deep learning model for acoustic propagation. Being data-driven it is independent of how the data is obtained and can be employed for learning various ocean acoustic phenomena. The CRAN model can learn a reduced-dimensional representation of physical data and can predict the system evolution efficiently. Two cases of increasing complexity are considered to demonstrate the generalization ability of the CRAN. The first case is a one-dimensional wave propagation with spatially-varying discontinuous initial conditions. The second case corresponds to a far-field transmission loss distribution in a two-dimensional ocean domain with depth-dependent sources. For both cases, the CRAN can learn the essential elements of wave propagation physics such as characteristic patterns while predicting long-time system evolution with satisfactory accuracy. Such ability of the CRAN to learn complex ocean acoustics phenomena has the potential of real-time prediction for marine vessel decision-making and online control.