Approximation of modal wavenumbers and group speeds in an oceanic waveguide using a neural network

Abstract

Underwater acoustic propagation is influenced not only by the property of the water column, but also by the seabed property. Modeling this propagation using normal mode simulation can be computationally intensive, especially for wideband signals. To address this challenge, a Deep Neural Network is used to predict modal horizontal wavenumbers and group velocities. Predicted wavenumbers are then used to compute modal depth functions and transmission losses, reducing computational cost without significant loss in accuracy. This is illustrated on a simulated Shallow Water 2006 inversion scenario.