<i>Computational Ocean Acoustics</i>

Jensen, Finn B.; Kuperman, W. A.; Porter, Michael B.; Schmidt, Henrik; Bartram, James F.

doi:10.1121/1.411832

Cited by 74 publications

(90 citation statements)

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“…The rise of the dispersion-based inversion schemes popularity can be associated with the so-called warping transform [5,8] offering a very elegant way to separate the modal components of a pulse signal. At the same time, for any fixed model of a waveguide dispersion curves can be computed theoretically by solving acoustic spectral problem [19,26]. The mismatch between the experimental and theoretical arrival times indicates to what extent the given model of the waveguide is consistent with the observation results.…”

Section: Dispersion-based Geoacoustic Inversionmentioning

confidence: 64%

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A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

et al. 2017

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Abstract:A volunteer computing project aimed at solving computationally hard inverse problems in underwater acoustics is described. This project was used to study the possibilities of the sound speed profile reconstruction in a shallow-water waveguide using a dispersion-based geoacoustic inversion scheme. The computational capabilities provided by the project allowed us to investigate the accuracy of the inversion for different mesh sizes of the sound speed profile discretization grid. This problem suits well for volunteer computing because it can be easily decomposed into independent simpler subproblems.

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Section: Dispersion-based Geoacoustic Inversionmentioning

confidence: 64%

“…. M, where τm(f ) denotes the arrival time of m-th modal component [19,23] of a pulse acoustical signal as a function of frequency f (M is the total number of modes that can be filtered from the data). The curves t = τm(f ) in the two-dimensional time-frequency space are called dispersion curves [20].…”

Section: Dispersion-based Geoacoustic Inversionmentioning

confidence: 99%

A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

et al. 2017

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“…One of the issues in studying these systems is that shallow-water acoustic modeling is inherently complex (Jensen et al 2000). The steep walls of the narrow fjords of Glacier Bay further complicate the matter, creating conditions in which empirical measurements of acoustic propagation are invaluable.…”

Section: Introductionmentioning

confidence: 99%

Predicting the acoustic exposure of humpback whales from cruise and tour vessel noise in Glacier Bay, Alaska, under different management strategies

Frankel¹,

Gabriele²

2017

Endang. Species. Res.

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Vessel traffic management regimes intended to protect baleen whales can have unexpected consequences on whale exposure to underwater noise. Using the Acoustic Integration Model, we simulated whale and vessel movements in Glacier Bay National Park (GBNP). We estimated vessel noise exposures to humpback whales Megaptera novaeangliae while varying the number, speed (13 vs. 20 knots [kn]), and timing of cruise ships, and keeping a constant number, speed, and timing of smaller tour vessels. Using calibrated noise signatures for each vessel and the known sound velocity profile and bathymetry of Glacier Bay, we estimated received sound levels for each simulated whale every 15 s in a 24 h period. Simulations with fast ships produced the highest maximal sound pressure level (MSPL) and cumulative sound exposure levels (CSEL). Ships travelling at 13 kn produced CSEL levels 3 times lower than those traveling at 20 kn. We demonstrated that even in cases where a ship is only a few dB quieter at a slower speed, CSEL is lower, but the ship's transit may take substantially longer. Synchronizing ship arrival times had little effect on CSEL or MSPL but appreciably decreased cumulative sound exposure time (CSET). Overall, our results suggest that the most effective way to reduce humpback whale acoustic exposure in GBNP is to reduce the numbers of cruise ships or their speed, although adjusting ship schedules may also be beneficial. Marine protected area managers may find these results illustrative or adapt these methods to better understand the acoustic effects of specific vessel management circumstances.

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“…The reconstruction of the impulse response function is also well suited for MFP, which has been extensively used in underwater acoustics, seismology [19,26,27], and in an initial application of structural vibrations [34] and damage detection in homogeneous and inhomogeneous plates [23,24]. In MFP, the response in a sparse array is matched to a model, or replica, containing the known solution to the response in a particular spatial 'look direction'.…”

Section: (B) Matched-field Processingmentioning

confidence: 99%

“…With the addition of using the passively recovered impulse response function, the detectability of damage is studied using similar variations of reciprocity and beamforming techniques in NDE [21][22][23][24][25] with the addition of signal processing techniques well studied in ocean acoustics [19,26,27].…”

Section: Introductionmentioning

confidence: 99%

Application of damage detection methods using passive reconstruction of impulse response functions

Tippmann

Zhu

Scalea

2015

Phil. Trans. R. Soc. A.

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In structural health monitoring (SHM), using only the existing noise has long been an attractive goal. The advances in understanding cross-correlations in ambient noise in the past decade, as well as new understanding in damage indication and other advanced signal processing methods, have continued to drive new research into passive SHM systems. Because passive systems take advantage of the existing noise mechanisms in a structure, offshore wind turbines are a particularly attractive application due to the noise created from the various aerodynamic and wave loading conditions. Two damage detection methods using a passively reconstructed impulse response function, or Green's function, are presented. Damage detection is first studied using the reciprocity of the impulse response functions, where damage introduces new nonlinearities that break down the similarity in the causal and anticausal wave components. Damage detection and localization are then studied using a matched-field processing technique that aims to spatially locate sources that identify a change in the structure. Results from experiments conducted on an aluminium plate and wind turbine blade with simulated damage are also presented.

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Computational Ocean Acoustics

Cited by 74 publications

References 0 publications

A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

Predicting the acoustic exposure of humpback whales from cruise and tour vessel noise in Glacier Bay, Alaska, under different management strategies

Application of damage detection methods using passive reconstruction of impulse response functions

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