A Bayesian framework for geoacoustic inversion of wind-driven ambient noise in shallow water
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]
|Collections||ANU Research Publications|
|Source:||Canadian Acoustics Vol 40 - Number 3 Proceedings of the Acoustics Week in Canada 2011|
|01_Quijano_A_Bayesian_framework_for_2012.pdf||201.56 kB||Adobe PDF||Request a copy|
|02_Quijano_A_Bayesian_framework_for_2012.pdf||103.79 kB||Adobe PDF||Request a copy|
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