Leveraging sound speed dynamics and generative deep learning for ray-based ocean acoustic tomography.

Abstract

A generative deep learning framework is introduced for ray-based ocean acoustic tomography (OAT), an inverse problem for estimating sound speed profiles (SSP) based on arrival-times measurements between multiple acoustic transducers, which is typically ill-posed. This framework relies on a robust low-dimensional parametrization of the expected SSP variations using a variational autoencoder and a linear dynamical model as further regularization. This framework was tested using SSP variations simulated by a regional ocean model with submesoscale permitting horizontal resolution and various transducer configurations spanning the upper ocean over short propagation ranges and was found to outperform conventional linear least squares formulations of OAT.