Measurements of acoustic ambient noise in shallow water due to breaking surf

Wilson, O. B.; Wolf, Stephen N.; Ingenito, F.

doi:10.1121/1.392557

Cited by 49 publications

(22 citation statements)

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“…However, there is very little literature on the horizontal directionality of ambient noise and Urick (1983) reported 'few or no observations'. The horizontal directionality of noise due to surf and ice movement (Wilson et al 1985, Yang et al 1987, Deane 2000 has been described, but no reports of horizontal directionality due to different shore types in coastal waters appear to exist.…”

Section: Introductionmentioning

confidence: 99%

Localised coastal habitats have distinct underwater sound signatures

Radford¹,

Stanley²,

Tindle³

et al. 2010

Mar. Ecol. Prog. Ser.

182

133

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Section: Introductionmentioning

confidence: 99%

Localised coastal habitats have distinct underwater sound signatures

Radford¹,

Stanley²,

Tindle³

et al. 2010

Mar. Ecol. Prog. Ser.

182

133

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“…This may be a cue related to the proximity (<10 km) of crashing waves oriented parallel to the coastline. The sound from waves crashing on shore is likely within the detectable range of sharks (40− 800 Hz, Myrberg 2001), up to 9 km away from the shoreline (Wilson et al 1985). Therefore, the sound of crashing waves could aid in navigation (Montgomery , and be a possible explanation for the selection of near-shore waters.…”

Section: Habitat Selectionmentioning

confidence: 99%

Habitat selection of a coastal shark species estimated from an autonomous underwater vehicle

Haulsee¹,

Breece²,

Miller³

et al. 2015

Mar. Ecol. Prog. Ser.

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Quantifying habitat selection in marine organisms is challenging because it is difficult to obtain species location information with multiple corresponding habitat measurements. In the ocean, habitat conditions vary on many spatiotemporal scales, which have important consequences for habitat selection. While macroscale biotic and abiotic features influence seasonal movements (spatial scales of 100−1000 km), selectivity of conditions on mesoscales (1−100 km) reflects an animal's response to the local environment. In this study, we examined habitat selectivity by pairing acoustic telemetry with environmental habitat parameters measured by an autonomous underwater vehicle (AUV), and demonstrate that migrating sand tiger sharks Carcharias taurus along the East Coast of the USA did not randomly use the coastal environment. Of the variables examined, we found evidence to suggest that sand tigers were selecting their habitat based on distance to shore, salinity, and colored dissolved organic matter (CDOM). Notably, temperature was not predictive of habitat use in our study. We posit that during their coastal migration, sand tigers select for specific mesoscale coastal habitats that may inform navigation or feeding behaviors. To our knowledge, this is the first empirical measure of mesoscale habitat selection by a coastal marine organism using an AUV. The applications of this method extend beyond the habitat selectivity of sand tigers, and will prove useful for future studies combining in situ observations of marine habitats and animal observations.

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“…Acoustic vector-sensor arrays have attracted much attention in recent years. The U.S. Navy has been using vector-sensor arrays in the form of "DIFAR" (directional low frequency analysis and recording sonobuoys) to study the acoustic properties of the ocean 11 and researchers have been using them to study marine life. 12 A typical vector-sensor measurement consists of a set of measurements of acoustic particle velocities (first order derivatives of the acoustic pressure) as well as the measurement of the omnidirectional acoustic pressure.…”

Section: Introductionmentioning

confidence: 99%

Fast implementation of sparse iterative covariance-based estimation for source localization

Zhang

Abeida

Xue

et al. 2012

The Journal of the Acoustical Society of America

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Fast implementations of the sparse iterative covariance-based estimation (SPICE) algorithm are presented for source localization with a uniform linear array (ULA). SPICE is a robust, user parameter-free, high-resolution, iterative, and globally convergent estimation algorithm for array processing. SPICE offers superior resolution and lower sidelobe levels for source localization compared to the conventional delay-and-sum beamforming method; however, a traditional SPICE implementation has a higher computational complexity (which is exacerbated in higher dimensional data). It is shown that the computational complexity of the SPICE algorithm can be mitigated by exploiting the Toeplitz structure of the array output covariance matrix using Gohberg-Semencul factorization. The SPICE algorithm is also extended to the acoustic vector-sensor ULA scenario with a specific nonuniform white noise assumption, and the fast implementation is developed based on the block Toeplitz properties of the array output covariance matrix. Finally, numerical simulations illustrate the computational gains of the proposed methods.

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Measurements of acoustic ambient noise in shallow water due to breaking surf

Cited by 49 publications

References 0 publications

Localised coastal habitats have distinct underwater sound signatures

Localised coastal habitats have distinct underwater sound signatures

Habitat selection of a coastal shark species estimated from an autonomous underwater vehicle

Fast implementation of sparse iterative covariance-based estimation for source localization

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