Bayesian geoacoustic inversion and source tracking for horizontal line array data

Abstract

This thesis develops non-linear Bayesian methods for three-dimensional tracking of a moving acoustic source in shallow water despite environmental uncertainty, with application to data from a horizontal line array (HLA) of hydrophones. Focalization-tracking maximizes the posterior probability density (PPD) over track and environmental parameters. Synthetic test cases show that the algorithm substantially outperforms tracking with poor environmental estimates and generally obtains results close to those achieved with exact environmental knowledge. Marginalization-tracking integrates the PPD over environmental parameters to obtain joint marginal distributions over source coordinates, from which track uncertainty estimates and the most probable track are extracted. Both approaches are applied to HLA data from a shallow-water experiment conducted in the Barents Sea. Focalization-tracking successfully estimates the tracks of a towed source and a surface ship in cases where simpler tracking algorithms fail. Marginalization-tracking generally outperforms focalization-tracking and gives uncertainty estimates that encompass the true tracks. As a precursor, Bayesian geoacoustic inversion is applied (to both controlled-source and ship-noise data) to estimate seabed model parameters and their uncertainties. It is demonstrated that combining data from multiple, independent time-series segments in the inversion can significantly reduce geoacoustic parameter uncertainties. Geoacoustic uncertainties are also shown to depend on ship range and orientation.