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This paper presents data from mid-frequency (4-8 kHz) under-ice acoustic transmission and communication experiments in the Nansen Basin and in the Beaufort Sea of the Arctic Ocean. Measured impulse responses to range â¼10 km showed considerable delay spread between distinct groups of arrivals due to surface-ducted and bottom-reflected paths. The relative strengths of these groups depend on duct and under-ice properties. We demonstrate that despite shallow (â¼25 m) source/receiver depths, good communication performance can be achieved in both environments when exploiting the bottom-reflected arrivals. Simulations with the Bellhop ray model support the findings.
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This paper presents analysis of a 1-year (2018-2019) recording of ambient noise (40-2000 Hz) at a seasonally ice-covered location on the continental slope between the Svalbard archipelago and the Nansen Basin, northeast Atlantic Arctic. Time series of ambient noise show highest correlations with ice concentration and wind speed. A log-wind speed regression model is fitted to spectral noise data for three categories of ice concentration. Wind-speed dependence decreases with increasing ice concentration and increases with frequency, except at high ice concentration. Periodicity in noise during the ice-covered season is related to the M2 and M4 tidal current constituents.
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This paper applies a non-linear Bayesian marginalization approach to ship spectral source level estimation in shallow water with unknown seabed properties and uncertain source depth. The algorithm integrates the posterior probability density over seabed models sampled via trans-dimensional Bayesian matched-field inversion and over depths/ranges of multiple point sources (representing different noise-generating components of a large ship) via Metropolis-Hastings sampling. Source levels and uncertainty are derived from marginal distributions for source strength. The approach is applied to radiated noise due to a container ship recorded on a bottom-moored horizontal array in shallow water. The average uncertainty is 3.8 dB/Hz for tonal frequencies.
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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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A sonobuoy field was deployed in the Marginal Ice Zone of the Fram Strait in June 2011 to study the spatial variability of ambient noise. High noise levels observed at 10-200 Hz are attributed to distant (1400 km range) seismic exploration. The noise levels decreased with range into the ice cover; the reduction is fitted by a spreading loss model with a frequency-dependent attenuation factor less than for under-ice interior Arctic propagation. Numerical modeling predicts transmission loss of the same order as the observed noise level reduction and indicates a significant loss contribution from under-ice interaction.
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This letter develops a Bayesian focalization approach for three-dimensional localization of an unknown number of sources in shallow water with uncertain environmental properties. The algorithm minimizes the Bayesian information criterion using adaptive hybrid optimization for environmental parameters, Metropolis sampling for source bearing, and Gibbs sampling for source ranges and depths. Maximum-likelihood expressions are used for unknown complex source strengths and noise variance, which allows these parameters to be sampled implicitly. An efficient scheme for adding/deleting sources is used during the optimization. A synthetic example considers localizing a quiet source in the presence of multiple interferers using a horizontal line array.
Asunto(s)
Acústica , Oceanografía/métodos , Procesamiento de Señales Asistido por Computador , Sonido , Incertidumbre , Agua , Acústica/instrumentación , Algoritmos , Teorema de Bayes , Simulación por Computador , Diseño de Equipo , Movimiento (Física) , Oceanografía/instrumentación , Océanos y Mares , Presión , Espectrografía del Sonido , Factores de Tiempo , Transductores de PresiónRESUMEN
This paper develops a non-linear Bayesian marginalization approach for three-dimensional source tracking in shallow water with uncertain environmental properties. The algorithm integrates the posterior probability density via a combination of Metropolis-Hastings sampling over environmental and bearing model parameters and Gibbs sampling over source range/depth, with track constraints on source velocity applied. Marginal distributions for source range/depth and source bearing are derived, with source position uncertainties estimated from the distributions. The Viterbi algorithm is applied to obtain the most probable three-dimensional track. The approach is applied to experimental narrowband data recorded on a bottom-moored horizontal line array in the Barents Sea.
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Acústica , Teorema de Bayes , Imagenología Tridimensional , Dinámicas no Lineales , Agua de Mar , Procesamiento de Señales Asistido por Computador , Sonido , Algoritmos , Movimiento (Física) , Ruido del Transporte , Océanos y Mares , Navíos , Espectrografía del Sonido , Factores de TiempoRESUMEN
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.
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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.