Application of multilayered neural networks to ocean acoustic tomography inversions

Abstract

Ocean Acoustic Tomography (OAT) is a synoptic observation method to infer the internal structure of the Ocean. The underlying idea is to estimate the three-dimensional sound speed distribution by inverting the travel times of a sound signal propagated between fixed sources and receivers. Usual inversion methods are derived from a linearization around a reference sound speed profile. This paper presents a non linear inverse method. This method relies upon the ability of neural nets to learn a non linear relationship from examples. A set of realistic sound speed profiles is built and the arrival time patterns in given experimental conditions are computed from a ray-tracing model. The inverse mapping is learned from this set of examples by a standard multilayered network. When unlearned time patterns are presented to the net inputs, it estimates the corresponding sound speed environments. An application of this method to synthetic data is presented in this paper. Four ocean model parameters (sound velocity v...