Bayesian matched-field geoacoustic inversion

Authors

  • Stan E. Dosso School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8W 3P6, Canada

Keywords:

Acoustics, Covariance matrix, Functions, Monte Carlo methods, Parameter estimation, Uncertainty analysis, Geoacoustic inversion, Likelihood functions

Abstract

A Bayesian approach to matched-field geoacoustic inversion, with emphasis on rigorous uncertainty estimation Bayesian formulation, is discussed. Integration can be carried out using Markov-chain Monte Carlo importance sampling methods, such as fast Gibbs sampling. The data uncertainty distribution, which defines the likelihood function, must include both measurement errors and theory errors. Wide bounds were applied for the geoacoustic parameters to limit the inversion to physically reasonable values but allow the acoustic data to determine the solution. The KS test indicated no significant evidence against the assumption of Gaussian-distributed error processes, while the runs test suggested that the estimated data covariance matrices accounted for much, but not all, of the data error correlations.

Downloads

Published

2007-09-01

How to Cite

1.
Dosso SE. Bayesian matched-field geoacoustic inversion. Canadian Acoustics [Internet]. 2007 Sep. 1 [cited 2021 Jul. 29];35(3):178-9. Available from: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/1958

Issue

Section

Proceedings of the Acoustics Week in Canada

Most read articles by the same author(s)

1 2 3 > >>