Abstract
This paper considers the joint estimation of water-column sound-speed profiles (SSP) and seabed geoacoustic models through Bayesian inversion of modal-dispersion data (arrival time as a function of frequency from warping time-frequency analysis). The inversion is formulated in terms of separate trans-dimensional models for the water column and seabed to intrinsically parametrize each according to the information content of the data. An efficient reversible-jump Markov-chain Monte Carlo algorithm is applied to estimate marginal posterior probability profiles, quantifying the resolution of SSP and seabed structure. Measured and simulated dispersion data, as well as varying levels of prior information, are considered to examine the ability to estimate water-column and seabed profiles, and to investigate relationships between these.
Published Version
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