Probabilistic two-dimensional water-column and seabed inversion with self-adapting parameterizations.

This paper develops a probabilistic two-dimensional (2D) inversion for geoacoustic seabed and water-column parameters in a strongly range-dependent environment. Range-dependent environments in shelf and shelf-break regions are of increasing importance to the acoustical-oceanography community, and recent advances in nonlinear inverse theory and sampling methods are applied here for efficient probabilistic range-dependent inversion. The 2D seabed and water column are parameterized using highly efficient, self-adapting irregular grids which intrinsically match the local resolving power of the data and provide parsimonious solutions requiring few parameters to capture complex environments. The self-adapting parameterization is achieved by implementing the irregular grid as a trans-dimensional hierarchical Bayesian model with an unknown number of nodes which is sampled with the Metropolis-Hastings-Green algorithm. To improve sampling, population Monte Carlo is applied with a large number of interacting parallel Markov chains with adaptive proposal distributions. The inversion is applied to simulated data for a vertical-line array and several source locations to several kilometers range. Complex acoustic-pressure fields are computed using a parabolic equation model and results are considered in terms of 2D ensemble parameter estimates and credibility intervals.