Continual Learning of Range-Dependent Transmission Loss for Underwater
  Acoustic using Conditional Convolutional Neural Net
Indu Kant Deo Akash Venkateshwaran Rajeev K. Jaiman
There is a significant need for precise and reliable forecasting of the
far-field noise emanating from shipping vessels. Conventional full-order models
based on the Navier-Stokes equations are unsuitable, and sophisticated model
reduction methods may be ineffective for accurately predicting far-field noise
in environments with seamounts and significant variations in bathymetry. Recent
advances in reduced-order models, particularly those based on convolutional and
recurrent neural networks, offer a faster and more accurate alternative. These
models use convolutional neural networks to reduce data dimensions effectively.
However, current deep-learning models face challenges in predicting wave
propagation over long periods and for remote locations, often relying on
auto-regressive prediction and lacking far-field bathymetry information. This
research aims to improve the accuracy of deep-learning models for predicting
underwater radiated noise in far-field scenarios. We propose a novel
range-conditional convolutional neural network that incorporates ocean
bathymetry data into the input. By integrating this architecture into a
continual learning framework, we aim to generalize the model for varying
bathymetry worldwide. To demonstrate the effectiveness of our approach, we
analyze our model on several test cases and a benchmark scenario involving
far-field prediction over Dickin's seamount in the Northeast Pacific. Our
proposed architecture effectively captures transmission loss over a
range-dependent, varying bathymetry profile. This architecture can be
integrated into an adaptive management system for underwater radiated noise,
providing real-time end-to-end mapping between near-field ship noise sources
and received noise at the marine mammal's location.