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