This paper presents results for matched field Bayesian geoacoustic inversion of multitonal continuous wave data collected on the New Jersey continental shelf. To account for effects of significant spatial and temporal variation of the water column sound speed, the sound speed profile was represented by empirical orthogonal functions. Data error information for the inversion was estimated from multiple time windows of the data. Inversion results for the sediment sound speeds at three ranges are in excellent agreement with the ground truth.

This paper investigates the influence of water column variability on the estimates of geoacoustic model parameters obtained from matched field inversions. The acoustic data were collected on the New Jersey continental shelf during shallow water experiments in August 2006. The oceanographic variability was evident when the data were recorded. To quantify the uncertainties of the geoacoustic parameter estimates in this environment, Bayesian matched field geoacoustic inversion was applied to multi-tonal continuous wave data. The spatially and temporally varying water column sound speed is parametrized in terms of empirical orthogonal functions and included in the inversion. Its impact on the geometric and geoacoustic parameter estimates is then analyzed by the inter-parameter correlations. Two different approaches were used to obtain information about the variation of the water sound speed. One used only the profiles collected along the experimental track during the experiment, and the other also included observations collected over a larger area. The geoacoustic estimates from both the large and small sample sets are consistent. However, due to the diversity of the oceanic sound speed, more empirical orthogonal functions are needed in the inversion when more sound speed profile samples are used.

This paper describes geoacoustic inversion of low frequency air gun data acquired during an experiment on the New Jersey shelf. Hybrid optimization and Bayesian inversion techniques based on matched field processing were applied to multiple shots from three air gun data sets recorded by a vertical line array in a long-range shallow water geometry. For the Bayesian inversions, full data error covariance matrix was estimated from a set of consecutive shots that had high temporal coherence and small spatial variation in source position. The effect of different data error information on the geoacoustic parameter uncertainty estimates was investigated by using the full data error covariance matrix, a diagonalized version of the full error covariance, and a diagonal matrix with identical variances. The comparison demonstrated that inversion using the full data error information provided the most reliable parameter uncertainty estimates. The inversions were highly sensitive to the near sea floor geoacoustic parameters, including sediment attenuation, of a simple single-layer geoacoustic model. The estimated parameter values of the model were consistent with depth averaged values (over wavelength scales) of a high resolution geoacoustic model developed from extensive ground truth information. The interpretation of the frequency dependence of the estimated attenuation is also discussed.

This paper presents travel time geoacoustic inversion of broadband data collected on a vertical line array at short range of 230m during the Shallow Water 2006 experiments. A ray-tracing method combined with a hybrid optimization algorithm that utilizes differential evolution and downhill simplex was used for the inversion of sediment properties. The ocean sound speed profile and geometric parameters were inverted prior to the sea bottom properties to account for the temporally variable ocean environment. The sediment sound speed and thickness estimates are consistent with in situ measurements and matched-field inversion results of longer-range data from the experiment.

Assimilating oceanic observations into prediction systems is an advantageous approach for real‐time ocean environment characterization. However, its benefits to underwater acoustic predictions are not trivial due to the nonlinearity and sensitivity of underwater acoustic propagation to small‐scale oceanic features. In order to assess the potential of oceanic data assimilation, integrated ocean‐acoustic Observing System Simulation Experiments are conducted. Synthetic altimetry and in situ data were assimilated through a variational oceanographic data assimilation system. The predicted sound speed fields are then ingested in a range‐dependent acoustic model for transmission loss (TL) predictions. The predicted TLs are analyzed for the purpose of (i) evaluating the contributions of different sources to the uncertainties of oceanic and acoustic forecasts and (ii) comparing the impact of different oceanic analysis schemes on the TL prediction accuracy. Using ensemble member clustering techniques, the contributions of boundary conditions, ocean parameterizations, and geoacoustic characterization to acoustic prediction uncertainties are addressed. Subsequently, the impact of three‐dimensional variational (3DVAR), 4DVAR, and hybrid ensemble‐3DVAR data assimilation on acoustic TL prediction at two signal frequencies (75 and 2,500 Hz) and different ranges (30 and 60 km) are compared. 3DVAR significantly improves the predicted TL accuracy compared to the control run. Promisingly, 4DVAR and hybrid data assimilation further improve the TL forecasts, the hybrid scheme achieving the highest skill scores for all cases, while being the most computationally intensive scheme. The optimal scheme choice thus depends on requirements on the accuracy and computational constraints. These findings foster developments of coupled data assimilation for operational underwater acoustic propagation.