| Título: | A bayesian framework for geoacoustic inversion of wind-driven ambient noise in shallow water |
| Autores: |
Jorge, E. Quijano; School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada Stan, E. Dosso; School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada Dettmer, Jan; School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada |
| Fecha: | 2012-09-01 |
| Publicador: | Canadian Acoustics |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Conference Proceedings Actes de congrès |
| Tema: |
Digital storage 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 |
| Descripción: | 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. |
| Idioma: | Inglés |
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