This paper applies Bayesian inversion to bottom-loss data derived from wind-driven ambient noise measurements from a vertical line array to quantify the information content constraining seabed geoacoustic parameters. The inversion utilizes a previously proposed ray-based representation of the ambient noise field as a forward model for fast computations of bottom loss data for a layered seabed. This model considers the effect of the array's finite aperture in the estimation of bottom loss and is extended to include the wind speed as the driving mechanism for the ambient noise field. The strength of this field relative to other unwanted noise mechanisms defines a signal-to-noise ratio, which is included in the inversion as a frequency-dependent parameter. The wind speed is found to have a strong impact on the resolution of seabed geoacoustic parameters as quantified by marginal probability distributions from Bayesian inversion of simulated data. The inversion method is also applied to experimental data collected at a moored vertical array during the MAPEX 2000 experiment, and the results are compared to those from previous active-source inversions and to core measurements at a nearby site.

Active sonar systems can provide good target detection potential but are limited in shallow water environments by the high level of reverberation produced by the interaction between the acoustic signal and the ocean bottom. The nature of the reverberation is highly variable and depends critically on the ocean and seabed properties, which are typically poorly known. This has motivated interest in techniques that are invariant to the environment. In passive sonar, a scalar parameter termed the waveguide invariant, has been introduced to describe the slope of striations observed in lofargrams. In this work, an invariant for active sonar is introduced. This active invariant is shown to be present in the time-frequency structure observed in sonar data from the Malta Plateau, and the structure agrees with results produced from normal mode simulations. The application of this feature in active tracking algorithms is discussed.

This letter applies trans-dimensional Bayesian geoacoustic inversion to quantify the uncertainty due to model selection when inverting bottom-loss data derived from wind-driven ambient-noise measurements. A partition model is used to represent the seabed, in which the number of layers, their thicknesses, and acoustic parameters are unknowns to be determined from the data. Exploration of the parameter space is implemented using the Metropolis–Hastings algorithm with parallel tempering, whereas jumps between parameterizations are controlled by a reversible-jump Markov chain Monte Carlo algorithm. Sediment uncertainty profiles from inversion of simulated and experimental data are presented.

The seabed reflection loss (shortly "bottom loss") is an important quantity for predicting transmission loss in the ocean. A recent passive technique for estimating the bottom loss as a function of frequency and grazing angle exploits marine ambient noise (originating at the surface from breaking waves, wind, and rain) as an acoustic source. Conventional beamforming of the noise field at a vertical line array of hydrophones is a fundamental step in this technique, and the beamformer resolution in grazing angle affects the quality of the estimated bottom loss. Implementation of this technique with short arrays can be hindered by their inherently poor angular resolution. This paper presents a derivation of the bottom reflection coefficient from the ambient-noise spatial coherence function, and a technique based on this derivation for obtaining higher angular resolution bottom-loss estimates. The technique, which exploits the (approximate) spatial stationarity of the ambient-noise spatial coherence function, is demonstrated on both simulated and experimental data.

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Research on the propagation of acoustic waves in the ocean bottom sediment is of interest for active sonar applications such as target detection and remote sensing. The interaction of acoustic energy with the sea floor sublayers is usually modeled with techniques based on the full solution of the wave equation, which sometimes leads to mathematically intractable problems. An alternative way to model wave propagation in layered media containing random scatterers is the radiative transfer (RT) formulation, which is a well established technique in the electromagnetics community and is based on the principle of conservation of energy. In this paper, the RT equation is used to model the backscattering of acoustic energy from a layered elastic bottom sediment containing distributions of independent scatterers due to a constant single frequency excitation in the water column. It is shown that the RT formulation provides insight into the physical phenomena of scattering and conversion of energy between waves of different polarizations.

This paper develops a fast numerical approach to computing spherical-wave reflection coefficients (SWRCs) for layered seabeds, which provides substantial savings in computation time when used as the forward model for geoacoustic inversion of broadband seabed reflectivity data. The approach exploits the Sommerfeld-integral representation of SWRCs as the Hankel transform of a function proportional to the plane-wave reflection coefficient (PWRC), and applies Levin integration to the rapidly oscillating integrand cast as the product of a (pre-computed) media-independent matrix and a vector involving PWRCs at a sparse sampling of integration angles. Compared to conventional Simpson's rule integration for computation of the SWRC, the Levin integration yields speed-up factors of an order of magnitude or more. Further, it results in reduced memory requirements for storage of pre-computed quantities, a desirable property when a graphics processing unit (GPU) is used for parallel computation of SWRCs. The paper applies trans-dimensional Bayesian inversion to investigate the impact of forward modeling in terms of PWRCs and SWRCs on the estimation of geoacoustic parameters and uncertainties. Model comparisons are quantified in simulated- and measured-data inversions by comparing the estimated geoacoustic parameters to the true parameters or core measurements, respectively, and by calculating the deviance information criterion for model selection.

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