Layered and gradient model parameterizations in geoacoustic inversion

Abstract

This paper considers the importance of model parameterization in geoacoustic inversion and uncertainty estimation, including quantitative approaches to model selection as well as potential limitations of the information content of acoustic data to determine the form of geoacoustic profiles, e.g., differentiating between layered and gradient structures. In particular, general parameterizations are considered based on trans-dimensional (trans-D) inversion, which represents profiles as an unknown number of uniform layers, and Bernstein polynomial (BP) inversion, which represents smooth gradients as polynomials of unknown order. These approaches are illustrated and compared for the inversion of high-order modal-dispersion data collected at the New England Mud Patch. It is shown that while the data constrain the sound-speed profile in the mud layer to reasonably high precision, the data cannot differentiate between trans-D layered or BP gradient representations. However, simpler (fixed) parameterizations, such as a homogeneous layer or linear gradient, can be ruled out based on the Bayesian information criterion. Furthermore, the prior choice of parameterization (layers or gradient) has implications on whether the sound-speed ratio at the water–sediment interface is estimated to be less or greater than one with high probability (an issue other acoustic datasets may share). [Work supported by the Office of Naval Research]