In a viscoelastic Earth, stresses slowly built up due to fault locking are relaxed concurrently during the entire interseismic period. This interseismic stress relaxation causes crustal deformation much farther away from the locked fault than can be explained using elastic models that neglect the relaxation. Here we develop a viscoelastic geodetic inversion model to address this problem at Cascadia. We invert ~500 horizontal velocity vectors based on continuous and campaign geodetic measurements over the past two decades. Ambiguities arising from long‐term rotation of upper‐plate crustal blocks are addressed by test‐correcting the geodetic velocities with two different block‐motion models. Fault back slip (i.e., slip deficit) Green's functions are derived using a Maxwell viscoelastic finite element model with realistic subduction zone structure and megathrust geometry. The preferred model features a narrow and shallow megathrust locked zone, consistent with earlier thermorheological reasoning. For an elastic model to fit the data to the same fidelity, megathrust locking has to extend to much greater depths. However, even with the viscoelastic model, the land‐based geodetic data still cannot resolve whether there is some creep (incomplete locking) in the shallowest part of the megathrust far offshore. Neither can the land data fully resolve along‐strike variations of the locking state. These ambiguities can be resolved only when adequate seafloor geodetic data are obtained.

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 develops a Bayesian approach for two related inverse problems: tracking an acoustic source when ocean environmental parameters are unknown, and determining environmental parameters using acoustic data from an unknown (moving) source. The formulation considers source and environmental parameters as unknown random variables constrained by noisy acoustic data and by prior information on parameter values (e.g., physical limits for environmental properties) and on inter-parameter relationships (limits on radial and vertical source speed). The goal is not simply to estimate parameter values, but to rigorously determine parameter uncertainty distributions, thereby quantifying the information content of the data/prior to resolve source and environmental parameters. Results are presented as marginal posterior probability densities (PPDs) for environmental parameters and joint marginal PPDs for source ranges and depths. Given the numerically intensive inversion, an efficient Markov-chain Monte Carlo importance-sampling approach is developed which combines Metropolis and heat-bath Gibbs’ sampling, employs efficient proposal distributions based on a linearized PPD approximation, and considers nonunity sampling temperatures to ensure a complete parameter search. The approach is illustrated with two simulated examples representing tracking a quiet submerged source and geoacoustic inversion using noise from an unknown ship of opportunity. In both cases, source, seabed, and water-column parameters are unknown.

Depth-dependent geoacoustic properties are inferred from wide-angle frequency-domain reflection-coefficient data at two sites with different mud-layer thicknesses on the New England Mud Patch. A trans-dimensional Bayesian inversion is employed to estimate geoacoustic properties and uncertainties from these data using the viscous grain shearing sediment model and spherical-wave reflection-coefficient predictions. Results near the thick-mud (SWAMI) site show a nearly uniform sound velocity over the upper approximately 9.2 m, followed by a transition layer with velocity increasing nonlinearly by $\sim$ 280 m/s over 1.8 m. At the thin-sediment (VC31-2) site, the velocity profile exhibits a similar transition layer. Estimates of intrinsic velocity and attenuation dispersion are also obtained. Over the measurement band of about 400–1300 Hz, the velocity in the fine-grained sediments (mud) at both sites varies by only a few meters per second, i.e., velocity is nearly independent of frequency. The attenuation of the fine-grained sediments at both sites follows a nearly linear frequency dependence. The geoacoustic inferences compare reasonably closely with independent measurements including core measurements, chirp-reflection data, and angle of intromission data.

This paper develops a linearized Bayesian inversion algorithm for high-precision localization of an autonomous underwater vehicle (AUV) on a test range using time difference of arrival acoustic data. The Bayesian approach allows the uncertainties of several factors, including precise receiver locations and bias and lateral variability of the water-column sound-speed profile, to be included in the inversion as unknown parameters with varying levels of prior information. This provides better estimates of these parameters (and hence AUV locations), and includes the effect of their uncertainties in the uncertainty estimates for AUV locations. A modeling study comparing AUV localization uncertainties from the linearized inversion that results from nonlinear Monte Carlo analysis shows that linearization errors are small, and hence linearized analysis is used to efficiently map AUV localization uncertainty as a function of position over the test range. These approaches are applied to model localization uncertainty for an AUV test range associated with the Victoria Experimental Network Under the Sea cabled observatory system, examining the factors mentioned above as well as the effects of water-column refraction and receiver geometry.

This paper presents single receiver geoacoustic inversion of a combustive sound source signal, recorded during the 2017 Seabed Characterization Experiment on the New England Mud Patch, in an area where water depth is around 70 m. There are two important features in this study. First, it is shown that high-order modes can be resolved and estimated using warping (up to mode number 18 over the frequency band 20–440 Hz). However, it is not possible to determine mode numbers from the data, so that classical inversion methods that require mode identification cannot be applied. To solve this issue, an inversion algorithm that jointly estimates geoacoustic properties and identifies mode number is proposed. It is successfully applied on a range-dependent track, and provides a reliable range-average estimation of geoacoustic properties of the mud layer, an important feature of the seabed on the experimental area.