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 address the challenge of dynamic ocean environments or unknown seabeds, the model is trained across different range-independent environments. Training data were generated using the Kraken normal mode code for shallow oceanic waveguides with variable environmental parameters and frequencies within 50-500 Hz. The proposed NN is conditioned on mode and frequency, enabling efficient broadband predictions. Evaluated on environments unseen during training, the NN accurately approximates modal parameters. Once trained, the proposed approach can reduce computation time for modal parameters by an order of magnitude compared to conventional codes, such as Kraken. This efficiency could support demanding applications, like geoacoustic inversion or simulations on computationally constrained platforms.