Convolutional recurrent autoencoder network for learning underwater
ocean acoustics
- URL: http://arxiv.org/abs/2204.05573v1
- Date: Tue, 12 Apr 2022 07:09:03 GMT
- Title: Convolutional recurrent autoencoder network for learning underwater
ocean acoustics
- Authors: Wrik Mallik, Rajeev K. Jaiman and Jasmin Jelovica
- Abstract summary: The CRAN architecture is a data-driven deep learning model for acoustic propagation.
The CRAN can learn a reduced-dimensional representation of physical data and can predict the system evolution efficiently.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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