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This thesis presents Bayesian geoacoustic inversion of seabed reflection-coefficient data as part of the U.S. Office of Naval Research Seabed Characterization Experiment 2017 at the New England Mud Patch. First, a linearized, ray-based Bayesian inversion of acoustic arrival times is carried out for high-precision estimation of experiment geometry and uncertainties, representing an important first step to inferring seabed properties using geoacoustic inversion methods such as reflection inversion. The highprecision estimates for source-receiver ranges, source depths, receiver depths, and water depths at reflection points along the survey track are used to calculate grazing angles, with angle uncertainties computed using Monte Carlo methods. The experiment geometry uncertainties are obtained using analytic linearized estimates, and verified with nonlinear analysis. Second, a trans-dimensional (trans-D) Bayesian inversion of reflection-coefficient data is carried out for geoacoustic parameters and uncertainties of fine-grained/cohesive sediments. The trans-D inversion samples probabilistically over an unknown number of seabed interfaces and the parameters of a zeroth-or first-order autoregressive error model. The numerical method of parallel tempering reversible jump Markov-chain Monte Carlo sampling is employed. Spherical-wave reflection coefficient modelling is applied using plane-wave decomposition in the Sommerfeld integral. The inversion provides marginal posterior probability profiles for Buckingham's viscous grain-shearing parameters: porosity, grain-to-grain compressional modulus, material exponent, and compressional viscoelastic time constant as a function of depth in the sediment. These parameters are used to compute dispersion relationships for each layer in the model, providing marginal posterior probability profiles for compressional-wave velocity and attenuation at different frequencies, as well as density. The geoacoustic inversion results are compared to independent mea-Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.

This paper presents nonlinear Bayesian inversion of wide-angle seabed reflection-coefficient data for fine-grained/cohesive sediments recorded in the ONR Seabed Characterization Experiment at the New England mud patch. The inversion is applied to high-resolution broadband reflectivity data from a site characterized by smooth bathymetry and a thick mud layer. Since smooth, continuous gradients in seabed properties are common for low-speed mud layers, this work develops a Bayesian inversion based on a Bernstein-polynomial representation of sediment geoacoustic profiles. The gradient-based Bernstein-polynomial inversion is compared to a trans-dimensional inversion which samples over profiles defined by an unknown number of discrete seabed layers. [The research was funded by the Office of Naval Research, Ocean Acoustics Program, and the Canadian Department of National Defence.]

The vast majority of sediment acoustics research has been aimed at understanding propagation in granular (sandy) sediments. The focus of the ONR Seabed Characterization Experiment was to improve understanding of fine-grained/cohesive sediments. A variety of measurement techniques by various researchers were conducted to that end. Here, we report on initial results from broadband wide-angle reflection measurements at two sites, one with a ∼2 m “mud” layer thickness and the other ∼11 m thick. The measured reflection coefficient exhibits features that permit estimation of geoacoustic properties including the critical angle (with a rather weak frequency dependence, 200-2500 Hz) and interference patterns in frequency-angle space (which provide information on properties in individual layers). Modeling permits insight into the information content of the data and some initial estimates of the geoacoustic properties. [This research was funded by the Office of Naval Research, Ocean Acoustics Program.]

This paper considers Bayesian geoacoustic inversion and uncertainty estimation for seabed profiles consisting of a smooth gradient of general form overlying a potentially-layered substrate. The profile gradient is parameterized as a linear combination of Bernstein-polynomial basis functions, with the polynomial order determined objectively by applying the deviance information criterion. In this way the form of the gradient is determined by the data, rather than by a subjective prior choice. The substrate below the gradient consists of uniform layers defined by an unknown number of (zero or more) interfaces, sampled probabilistically with trans-dimensional inversion. (Zero interfaces corresponds to a half-space with uniform parameter values equivalent to those at the base of the gradient.) The inversion approach is well suited to seabed environments consisting of an upper layer of soft, fine-grained sediments in which geoacoustic properties vary as a smooth function of depth overlying a harder, layered substrate, such as at the ONR Seabed Characterization Experiment at the New England mud patch. [Research funded by the Office of Naval Research, Ocean Acoustics Program.]