Bayesian matched-field geoacoustic inversion


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


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


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.



How to Cite

Dosso SE. Bayesian matched-field geoacoustic inversion. Canadian Acoustics [Internet]. 2007Sep.1 [cited 2021Apr.12];35(3):178-9. Available from:



Proceedings of the Acoustics Week in Canada