A deep learning approach to broadband modal propagation in various shallow water waveguides

Normal mode simulations of underwater acoustic propagation can be computationally intensive, particularly for broadband signals or iterative applications like inversion. An approach using neural network (NN) is introduced to approximate and accelerate these simulations. The NN predicts modal parameters, such as the horizontal wavenumbers and modal depth functions. Modal parameters are predicted and can subsequently be used to compute propagation for arbitrary source-receiver configurations. To a