Wind-driven underwater ambient noise for seabed remote sensing: A review of methods and results

Abstract

Estimation of seabed geoacoustic parameters in shallow water by acoustic remote sensing remains a challenging task due to constraints on hardware, data collection and analysis, and cost of maritime surveys. The work of Lisa Zurk and Martin Siderius, among many others, inspired investigation of signal processing techniques to process wind-driven underwater noise for the estimation of seabed geoacoustics. This paper presents a summary of the work on Bayesian geoacoustic inversion of ambient noise carried out at University of Victoria and Portland State University, using fixed and drifting vertical line arrays (VLA). The Bayesian inversion framework is based on Markov-chain Monte Carlo sampling. It estimates a joint posterior probability density function, from which marginal density functions, moments and covariances between geoacoustic parameters of interest can be quantified. The approach was applied to simulated and experimental ambient noise data for the estimation of seabed layer thicknesses, sound speeds, densities, and sediment attenuations. The resolution of the method was explored as a function of experiment factors such as array design and wind speed, and a method for extending the aperture of a VLA by extrapolating the noise coherence was proposed.