Model selection and Bayesian inference for high-resolutionseabed reflection inversion

摘要

This paper applies Bayesian inference, including model selection and posterior parameter inference,to inversion of seabed reflection data to resolve sediment structure at a spatial scale below the pulselength of the acoustic source. A practical approach to model selection is used, employing theBayesian information criterion to decide on the number of sediment layers needed to sufficiently fitthe data while satisfying parsimony to avoid overparametrization. Posterior parameter inference iscarried out using an efficient Metropolis—Hastings algorithm for high-dimensional models, andresults are presented as marginal-probability depth distributions for sound velocity, density, andattenuation. The approach is applied to plane-wave reflection-coefficient inversion of single-bouncedata collected on the Malta Plateau, Mediterranean Sea, which indicate complex fine structure closeto the water-sediment interface. This fine structure is resolved in the geoacoustic inversion results interms of four layers within the upper meter of sediments. The inversion results are in goodagreement with parameter estimates from a gravity core taken at the experiment site.