Many approaches to geoacoustic inversion are based implicitly on the assumptions that data errors are Gaussian-distributed and spatially uncorrelated (i.e., have a diagonal covariance matrix). However, the latter assumption is often not valid due to theory errors, and can lead to reduced accuracy for geoacoustic parameter estimates and underestimation of parameter uncertainties. This paper examines the effects of data error (residual) covariance in matched-field geoacoustic inversion. An inversion approach is developed based on a nonparametric method of estimating the full covariance matrix (including off-diagonal terms) from the data residuals and explicitly including this covariance in the misfit function. Qualitative and quantitative statistical tests for Gaussianity and for correlations in complex residuals are considered to validate the inversion results. The approach is illustrated for Bayesian geoacoustic inversion of broadband, vertical-array acoustic data measured in the Mediterranean Sea.

This paper applies the new method of fast Gibbs sampling (FGS) to estimate the uncertainties of seabed geoacoustic parameters in a broadband, shallow-water acoustic survey, with the goal of interpreting the survey results and validating the method for experimental data. FGS applies a Bayesian approach to geoacoustic inversion based on sampling the posterior probability density to estimate marginal probability distributions and parameter covariances. This requires knowledge of the statistical distribution of the data errors, including both measurement and theory errors, which is generally not available. Invoking the simplifying assumption of independent, identically distributed Gaussian errors allows a maximum-likelihood estimate of the data variance and leads to a practical inversion algorithm. However, it is necessary to validate these assumptions, i.e., to verify that the parameter uncertainties obtained represent meaningful estimates. To this end, FGS is applied to a geoacoustic experiment carried out at a site off the west coast of Italy where previous acoustic and geophysical studies have been performed. The parameter uncertainties estimated via FGS are validated by comparison with: (i) the variability in the results of inverting multiple independent data sets collected during the experiment; (ii) the results of FGS inversion of synthetic test cases designed to simulate the experiment and data errors; and (iii) the available geophysical ground truth. Comparisons are carried out for a number of different source bandwidths, ranges, and levels of prior information, and indicate that FGS provides reliable and stable uncertainty estimates for the geoacoustic inverse problem.

Self-noise geoacoustic inversion involves the estimation of bottom parameters such as sound speeds and densities by analyzing towed-array signals whose origin is the tow platform itself. As well as forming inputs to more detailed assessments of seabed geology, these parameters enable performance predictions for sonar systems operating in shallow-water environments. In this paper, Gibbs sampling is used to obtain joint and marginal posterior probability distributions for seabed parameters. The advantages of viewing parameter estimation problems from such a probabilistic perspective include better quantified uncertainties for inverted parameters as well as the ability to compute Bayesian evidence for a range of competing geoacoustic models in order to judge which model explains the data most efficiently.

Seabed geoacoustic variability is driven by geological processes that occur over a wide spectrum of space-time scales. While the acoustics community has some understanding of horizontal fine-scale geoacoustic variability, less than O(10(0)) m, and large-scale variability, greater than O(10(3)) m, there is a paucity of data resolving the geoacoustic meso-scale O(10(0)-10(3)) m. Measurements of the meso-scale along an ostensibly "benign" portion of the outer shelf reveal three classes of variability. The first class was expected and is due to horizontal variability of layer thicknesses: this was the only class that could be directly tied to seismic reflection data. The second class is due to rapid changes in layer properties and/or boundaries, occurring over scales of meters to hundreds of meters. The third class was observed as rapid variations of the angle/frequency dependent reflection coefficient within a single observation and is suggestive of variability at scales of meter or less. Though generally assumed to be negligible in acoustic modeling, the second and third classes are indicative of strong horizontal geoacoustic variability within a given layer. The observations give early insight into possible effects of horizontal geoacoustic variability on long-range acoustic propagation and reverberation.

The MAPEX2000 experiments were conducted in the Mediterranean Sea in March, 2000 to determine seabed properties using a towed acoustic source and receiver array. Towed systems are advantageous because they are easy to deploy from a ship and the moving platform offers the possibility for estimating spatially variable (range-dependent) seabed properties. In this paper, seabed parameters are determined using a matched-field geoacoustic inversion approach with measured, towed array data. Previous research has successfully applied matched-field geoacoustic inversion techniques to measured acoustic data. However, in nearly all cases the inverted data were collected on moored, vertical receiver arrays. Results here show that seabed parameters can also be extracted by inverting acoustic measurements from a towed array of receivers, and these agree with those inverted using data received simultaneously on a vertical array. These findings imply that a practical technique could be developed to map range-dependent seabed parameters over large areas using a towed acoustic system. An example of such a range-dependent inversion is given using measurements from the MAPEX2000 experiments.