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

E. Quijano Jorge, E. Dosso Stan, Jan Dettmer


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. Conventional beamforming was used to estimate the BL at 8 frequencies in the range 550 Hz to 1400 Hz. The BL data at 20 uniformly-spaced grazing angles from 14° to 90° is provided to the sequential Bayesian trans-D Monte Carlo algorithm for estimation of the PPD. The geoacoustic parameters and the depth of acoustic interfaces closely resemble the true profiles.


Acoustic noise; Acoustic wave transmission; Algorithms; Estimation; Ambient noise; Bayesian frameworks; Bayesian inversion; Continuous time; Conventional beamforming; Geoacoustic inversion; Geoacoustic parameters; Grazing angles; Markov chain Monte Carlo; Model parameterization; Monte carlo algorithms; Parameterizations; Posterior probability; Shallow waters

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