Abstract
This paper considers the information content to resolve both a geoacoustic model of the seabed and a model of the water-column sound-speed profile (SSP) in the inversion of modal-dispersion data. A Bayesian formulation of the inverse problem allows prior information representing varying levels of independent knowledge to be applied separately to the seabed and water-column models. Issues of interest include the extent to which knowledge (or lack thereof) of the SSP affects geoacoustic inversion, and the ability to remotely estimate the SSP from acoustic data in cases where the seabed is either well known or poorly known. The joint inversion for seabed and water-column properties is formulated in terms of a separate trans-dimensional model for each, providing an automated approach to quantitative model selection. The seabed model is formulated in terms of an unknown number of uniform sediment layers, while the SSP is formulated as an unknown number of depth/sound-speed nodes, with the reciprocal of sound speed squared varying linearly between nodes, as per normal-mode propagation models. The approach is applied to modal-dispersion data measured during the seabed characterization experiments at the New England Mud Patch. [Work supported by the Office of Naval Research]
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