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A Bayesian framework for geoacoustic inversion of wind-driven ambient noise in shallow water

Quijano, Jorge E.; Dosso, S.E.; Dettmer, Jan

Description

Bayesian inversion is applied to estimate the joint posterior probability density (PPD) of geoacoustic parameters. The PPD is sampled by a reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm, which uses an extended Metropolis-Hasting (MH) criterion that allows trans-D jumps between parameterizations, quantifying the uncertainly due to the lack of knowledge of the model parameterization. Sequential datsets are obtained by discretizing continuous-time recordings of ambient noise....[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Conference paper
URI: http://hdl.handle.net/1885/56019
Source: Canadian Acoustics Vol 40 - Number 3 Proceedings of the Acoustics Week in Canada 2011

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