This paper applies geoacoustic inversion to low-frequency narrow-band acoustic data from a quiet surface ship recorded on a bottom-moored horizontal line array in shallow water. A Bayesian matched-field inversion method is employed which quantifies geoacoustic uncertainties and allows for meaningful comparison of inversion results from different data sets. Geoacoustic inversion results for ship-noise data are compared with inversion results for multitone data from a towed controlled source collected in the same experiment, and with independent geophysical measurements. To increase the information content of low-level ship-noise data, the effect of including multiple, independent data segments in the inversion is investigated and shown to significantly reduce geoacoustic parameter uncertainties. Geoacoustic uncertainties are also shown to depend on ship range and orientation, with increased uncertainties for long ranges and for the ship stern oriented away from the array.
This paper applies geoacoustic inversion to acoustic-field data collected on a bottom-moored horizontal line array due to a continuous-wave towed source at a shallow water site in the Barents Sea. The source transmitted tones in the frequency band of 30–160Hz at levels comparable to those of a merchant ship, with resulting signal-to-noise ratios of 9–15dB. Bayesian inversion is applied to cross-spectral density matrices formed by averaging spectra from a sequence of time-series segments (snapshots). Quantifying data errors, including measurement and theory errors, is an important component of Bayesian inversion. To date, data error estimation for snapshot-averaged data has assumed either that averaging reduces errors as if they were fully independent between snapshots, or that averaging does not reduce errors at all. This paper quantifies data errors assuming that averaging reduces measurement error (dominated by ambient noise) but does not reduce theory (modeling) error, providing a physically reasonable intermediary between the two assumptions. Inversion results in the form of marginal posterior probability distributions are compared for the different approaches to data error estimation, and for data collected at several source ranges and bearings. Geoacoustic parameter estimates are compared with data from supporting geophysical measurements and historical data from the region.
This paper develops an approach to three-dimensional source tracking in an uncertain ocean environment using a horizontal line array (HLA). The tracking algorithm combines matched-field focalization for environmental (seabed and water column) and source-bearing model parameters with the Viterbi algorithm for range-depth estimation and includes physical constraints on source velocity. The ability to track a source despite environmental uncertainty is examined using synthetic test cases for various track geometries and with varying degrees of prior information for environmental parameters. Performance is evaluated for a range of signal-to-noise ratios in terms of the probability of estimating a track within acceptable position/depth errors. The algorithm substantially outperforms tracking with poor environmental estimates and generally obtains results close to those obtained with exact environmental knowledge. The approach is also applied to measured narrowband data recorded on a bottom-moored HLA in shallow water (the Barents Sea) and shown to successfully track both a towed submerged source and a surface ship in cases where simpler tracking algorithms failed.
This paper considers concurrent matched-field processing of data from multiple, spatially-separated acoustic arrays with application to towed-source data received on two bottom-moored horizontal line arrays from the SWellEx-96 shallow water experiment. Matched-field processors are derived for multiple arrays and multiple-snapshot data using maximum-likelihood estimates for unknown complex-valued source strengths and unknown error variances. Starting from a coherent processor where phase and amplitude is known between all arrays, likelihood expressions are derived for various assumptions on relative source spectral information (amplitude and phase at different frequencies) between arrays and from snapshot to snapshot. Processing the two arrays with a coherent-array processor (with inter-array amplitude and phase known) or with an incoherent-array processor (no inter-array spectral information) both yield improvements in localization over processing the arrays individually. The best results with this data set were obtained with a processor that exploits relative amplitude information but not relative phase between arrays. The localization performance improvement is retained when the multiple-array processors are applied to short arrays that individually yield poor performance.
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