title: Bayesian evidence computation for model selection in non-linear geoacoustic inference problems.
abstract: This paper applies a general Bayesian inference approach, based on Bayesian evidence computation, to geoacoustic inversion of interface-wave dispersion data. Quantitative model selection is carried out by computing the evidence (normalizing constants) for several model parameterizations using annealed importance sampling. The resulting posterior probability density estimate is compared to estimates obtained from Metropolis-Hastings sampling to ensure consistent results. The approach is applied to invert interface-wave dispersion data collected on the Scotian Shelf, off the east coast of Canada for the sediment shear-wave velocity profile. Results are consistent with previous work on these data but extend the analysis to a rigorous approach including model selection and uncertainty analysis. The results are also consistent with core samples and seismic reflection measurements carried out in the area.
authors.affiliation: School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. jand@uvic.ca
authors.forename: Jan
authors.initials: J
authors.lastname: Dettmer
authors.forename: Stan E
authors.initials: SE
authors.lastname: Dosso
authors.forename: John C
authors.initials: JC
authors.lastname: Osler
mesh_terms: D000162:Acoustics; D001499:Bayes Theorem; D003198:Computer Simulation; D019015:Geologic Sediments; D005844:Geology; D009038:Motion; D017711:Nonlinear Dynamics; D012623:Seawater; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography; D013997:Time Factors; D035501:Uncertainty