Trans-dimensional geoacoustic inversion and uncertainty quantification for SBCEX17 data

Abstract

This paper presents an efficient and general approach to Bayesian inversion and uncertainty quantification for seabed geoacoustic profile estimation. The model-selection problem of estimating an appropriate seabed parameterization is addressed with trans-dimensional (trans-D) inversion via reversible-jump Markov-chain Monte Carlo, which samples probabilistically over the number of layers. An efficient proposal density for parameter perturbations is based on using a linearized approximation to the posterior probability density, applied in principal-component (PC) space where the (rotated) parameters are uncorrelated. The acceptance rate of perturbations and birth/death steps is improved by parallel tempering, based on a series of interacting Markov chains with successively tempered (relaxed) likelihoods. The PC proposals are adapted individually to the tempering of each Markov chain. The data-error model is based on the assumption of multivariate Gaussian errors with correlations represented by an autoregressive process. The parameters of zeroth- and first-order autoregressive error processes are sampled trans-dimensionally to avoid over- or under-parameterizing the error model. The approach is illustrated for three data sets from the 2017 Seabed Characterization Experiment (SBCEX17), including broadband seabed reflection coefficients; dispersion of water-borne acoustic modes, resolved by warping analysis; and ship noise recorded at a bottom-mounted horizontal array of hydrophones.