Acoustic inversion methods using ship noise

In this thesis, acoustic inversion methods are employed to estimate array element locations and the geoacoustic properties of the seabed using measured acoustic data consisting of noise from a surface ship in the Gulf of Mexico. The array element localization utilizes relative travel-time informa...

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Bibliographic Details
Main Author: Morley, Michael G.
Other Authors: Chapman, N. Ross
Language:English
en
Published: 2007
Subjects:
Online Access:http://hdl.handle.net/1828/244
Description
Summary:In this thesis, acoustic inversion methods are employed to estimate array element locations and the geoacoustic properties of the seabed using measured acoustic data consisting of noise from a surface ship in the Gulf of Mexico. The array element localization utilizes relative travel-time information obtained by cross-correlating the recorded time series of ship noise received at spatially separated hydrophones. The relative travel-time data are used in an inversion, based on the regularized least-squares method and the acoustic ray tracing equations, to obtain improved estimates of the receiver and source positions and their uncertainties. Optimization and Bayesian matched-field inversion methods are employed to estimate seabed geoacoustic properties and their uncertainties in the vicinity of a bottom-moored vertical line array using the recorded surface ship noise. This study is used to test the feasibility of matched-field methods to detect temporal changes in the geoacoustic properties of the seabed near a known gas hydrate mound in the Gulf of Mexico. Finally, a synthetic study is performed that demonstrates how ignoring environmental range dependence of seabed sound speed and water depth in matched-field inversion can lead to biases in the estimated geoacoustic parameters. The study considers the distributions of optimal parameter estimates obtained from a large number of range-independent inversions of synthetic data generated for random range-dependent environments. Range-independent Bayesian inversions are also performed on selected data sets and the marginal parameter distributions are examined. Both hard- and soft-bottom environments are examined at a number of scales of variability in sound speed and water depth.