Abstract

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.

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