Probabilistic two dimensional joint water-column and seabed inversion

This paper develops a probabilistic two-dimensional (2D) inversion for geoacoustic seabed and water-column parameters in a strongly range-dependent environment. Range-dependent environments in shelf and shelf-break regions are of increasing importance t

Probabilistic two-dimensional water-column and seabed inversion with self-adapting parameterizations

This paper develops a probabilistic two-dimensional (2D) inversion for geoacoustic seabed and water-column parameters in a strongly range-dependent environment. Range-dependent environments in shelf and shelf-break regions are of increasing importance t

Efficient Bayesian multi-source localization using a graphical processing unit

This paper presents a highly-efficient approach to matched-field localization of an unknown number of ocean acoustic sources employing a graphics processing unit (GPU) for massively parallel computations. A Bayesian formulation is developed in which the number, locations, and complex spectra (amplitudes and phases) of multiple sources, as well as noise variance at each frequency, are considered unknown random variables constrained by acoustic data and prior information. The number of sources is determined during an initial burn-in stage by minimizing the Bayesian information criterion using an efficient birth/death scheme. Marginal posterior probability distributions for source locations are then computed using Gibbs sampling. Source and noise spectra are sampled implicitly by applying analytic maximum-likelihood solutions in terms of the source locations (explicit parameters). This greatly reduces the dimensionality of the inversion, but requires solving a very large number (order 10∧5) of complex matrix inversions for each sample of the explicit parameters. These inversions can be solved in parallel on a GPU, increasing efficiency by a factor of ∼100. Examples are given of localizing a large number of sources (up to 10) in near real time

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea

Studying the sea with sound

Because electromagnetic radiation is strongly attenuated in seawater while sound propagates efficiently to long (even global) ranges, scientists and engineers have devised many ingenious methods to use acoustics in the ocean in place of light, radio, an

Bayesian matched-field geoacoustic inversion

This paper describes a Bayesian approach to matched-field inversion (MFI) of ocean acoustic data for seabed geoacoustic properties. In a Bayesian formulation, the unknown environmental and experimental parameters are considered random variables constrained by noisy data and prior information, and the goal is to interpret the multi-dimensional posterior probability density (PPD). The PPD is typically characterized in terms of point estimates, marginal distributions, and posterior correlations (or joint statistics). Computing these requires numerical optimization and integration of the PPD, which are carried out efficiently here using adaptive hybrid optimization and Metropolis-Hastings sampling in principal-component space, respectively. Likelihood and misfit functions for multi-frequency MFI with incomplete source spectral information are derived based on the assumption of complex Gaussian-distributed data errors with covariance matrices estimated from residual analysis; posterior statistical tests are applied to validate these estimates and assumptions. Model selection is carried out by applying the Bayesian information criterion to determine the simplest seabed parameterization consistent with the resolving power of the data. Bayesian MFI is illustrated for shallow-water acoustic data measured in the Mediterranean Sea

Identifying Active Structures using Double-Difference Earthquake Relocations in Southwest British Columbia and the San Juan islands, Washington

This paper applies double-difference earthquake relocation techniques to investigate sources of seismicity in southwest British Columbia, Canada, and the San Juan Islands, Washington. The study area is a complex region of deformation and has the potential for large earthquakes in the North Americancrust. Double-difference earthquake relocation techniques are applied to identify otherwise-hidden active structures that may pose a hazard to nearby population and infrastructure. We present evidence for previously unrecognized active structures using precise relative earthquake relocations obtained using both catalog and waveform cross-correlation data. Results have significantly reduced errors over routine catalog locations and show lineations in areas of clustered seismicity. In southwest British Columbia, these lineations or streaks appear to be hidden structures that do not disrupt near-surface sediments; however, in the San Juan Islands the identified lineation could be related to recently mapped surface expressions of faults identified from seismic reflection and multibeam bathymetric surveys. We use a variety of velocity models for the relocations and find that inappropriate models lead to artifacts at layer boundaries and increased vertical errors

A Bayesian framework for geoacoustic inversion of wind-driven ambient noise in shallow water

Bayesian inversion is applied to estimate the joint posterior probability density (PPD) of geoacoustic parameters. The PPD is sampled by a reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm, which uses an extended Metropolis-Hasting (MH) criterion that allows trans-D jumps between parameterizations, quantifying the uncertainly due to the lack of knowledge of the model parameterization. Sequential datsets are obtained by discretizing continuous-time recordings of ambient noise. Conventional beamforming was used to estimate the BL at 8 frequencies in the range 550 Hz to 1400 Hz. The BL data at 20 uniformly-spaced grazing angles from 14° to 90° is provided to the sequential Bayesian trans-D Monte Carlo algorithm for estimation of the PPD. The geoacoustic parameters and the depth of acoustic interfaces closely resemble the true profiles

Bayesian evidence computation for model selection in non-linear geoacoustic inference problems

This paper applies a general Bayesian inference approach, based on Bayesian evidence computation, to geoacoustic inversion of interface-wave dispersion data. Quantitative model selection is carried out by computing the evidence (normalizing constants) for several model parameterizations using annealed importance sampling. The resulting posterior probability density estimate is compared to estimates obtained from Metropolis-Hastings sampling to ensure consistent results. The approach is applied to invert interface-wave dispersion data collected on the Scotian Shelf, off the east coast of Canada for the sediment shear-wave velocity profile. Results are consistent with previous work on these data but extend the analysis to a rigorous approach including model selection and uncertainty analysis. The results are also consistent with core samples and seismic reflection measurements carried out in the area