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Nov 1982

Volume 72, Issue S1, pp. S1-S108

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back to top Session T. Underwater Acoustics III: Scattering and Noise
Contributed Papers
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Backscattering of sound in shallow water (A)

T. G. Muir, L. A. Thompson, J. A. Shooter, and T. E. DeMary

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S35-S35 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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Measurements are reported on the backscattering of sound from an ocean surface and sand bottom overlying the continental shelf near San Diego, California. Data were acquired in the 1–25 kHz band with a directive parametric source at a grazing angle of 10°. The bottom was a ∼5‐m layer of coarse grained sand and clay (mean grain size 0.57 mm) that contained many shell fragments and sublayers. The bottom backscattering coefficient was found to be −40 dB re: 1 m2, independent of frequency. Surface backscattering was measured in a shoaling sea containing biologic scatterers. The surface heights were Gaussian distributed (rms wave height = 0.35 m) and had a power spectral density that peaked near 80 MHz and decayed as the fifth power of frequency. The surface backscattering coefficient was found to be −43 dB re: 1 m2, with no frequency dependence. Statistical data in the form of probability distributions (peak acoustic amplitudes in 500 sample ensembles at each frequency) show a Rayleigh distribution for bottom backscattering and high‐frequency (10–25 kHz) surface backscattering. Low‐frequency surface backscattering (1–5 kHz) departs from the Rayleigh form due to the prevalence of high‐amplitude echoes from fish. [Work supported by the Office of Naval Research.]
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Horizontal and vertical covariance of surface reverberation (A)

Gary R. Wilson

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S35-S35 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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Lake surface reverberation was measured simultaneously on four horizontally separated and nine vertically separated staves in two linear arrays. The vertical array was found to maintain a significant level of coherence at much larger separations than the horizontal array. The normalized horizontal covariance was constant with time, while the normalized vertical covariance oscillated slowly with a period that depended on the separation of the vertical staves. The measurements were compared to a theoretical model of reverberation by D. Middleton. The model was implemented with a uniform planar distribution of surface scatterers and adequately predicted the horizontal spatial covariance and the time dependence of both the horizontal and vertical covariance. However the simple planar surface model was unable to model the vertical spatial covariance, indicating a need for a more detailed description of the surface.
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Backscatter from the ocean surface at low frequency (A)

James E. Barger

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S35-S35 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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Backscatter strengths from the ocean surface were measured using a vertically directive, impulsive sound source having a 26‐Hz bandwidth centered at a frequency of 52 Hz. The ocean surface was characterized by a 6.3‐m swell having an 80‐m wavelength, with 15‐kt wind waves superposed. Backscatter strengths were measured as functions of the vertical grazing angle θ and of the horizontal angle ψ between the swell and the backscatter directions. The results are summarized by the expression for scattering strength, SS = −66.5 + 53 sin θ + 8.7 cos ψ. These values of scattering strength are somewhat larger than those reported by Brown and Saenger [M. V. Brown and R. A. Saenger, J. Acoust. Soc. Am. 52, 944–960 (1973)], but their data were not obtained from surfaces having such a large swell. The backscattered intensities have swell‐induced amplitude modulation, suggesting that backscatter from wind waves is periodically shadowed by swell troughs. [Work supported by DARPA.]
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Multiple scattering with applications to fish‐echo processing (A)

T. K. Stanton

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S35-S36 (1982); (2 pages)

Online Publication Date: 12 Aug 2005

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A general expression has been derived and evaluated for the backscattered echo energy of an acoustic pulse due to a collection of identical randomly distributed isotropic scatterers. Excess attenuation of the signal due to the extinction cross section of the scatterers as well as second‐order scattering have been taken into account. Special attention is focused toward the numerical evaluation of second‐order scattering effects. The expression is evaluated for three scattering geometries. It is shown in each geometry that when the absorption cross section of the scatterers is small, second‐order scattering at least partially offsets effects due to excess attenuation in the low‐to‐moderate attenuation region. It is shown that second‐order scattering can be important in fish‐echo processing where the echo energy is quite often analyzed. The directional characteristics, acoustic frequency of the pulse in relation to the resonance frequency of the swimbladder (if any), and the degree of randomness of the spatial distribution of the fish determine the degree to which second‐order scattering plays a role in this area. [Work supported by Office of Naval Research N00014‐80‐C‐0667.]
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Con‐ and de‐convolution of the fish scattering PDF and echo PDF for single transducer sonar (A)

C. S. Clay

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S36-S36 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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This paper is a continuation of our studies of acoustic methods to measure fish abundance. The integral equation for the probability density function (PDF) of fish echoes is in Clay and Medwin [Acoustical Oceanography, pp. 476–482 (1977)]. It relates the PDF of the output of the sonar wE(e) to the PDF of the sonar for a target at random location in the beam wI(b), and the PDF of the scattering process at the fish wR(e), where e is echo amplitude and b is the transducer response. Our procedure is to adjust parameters in the two‐parameter Rice PDF (the amplitude of the envelope of a sine wave plus noise) until wE(e) matches experimental data. Numerical computations are somewhat tedious. In this paper I use nonlinear transformations b = exp(x) and e = e0 exp(y) to change the integral to a standard form of the convolution integral. One can do a Fourier transformation of the result and then express the convolution as the product of z−− transforms. Computations with the z−− transforms are simple and may become the basis of ways to determine wF(e) directly. [Work supported by Office of Naval Research N00014‐80‐C‐0667.]
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Prediction of ocean basin bottom reverberation using a range‐dependent reverberation model (A)

K. R. Nicolas, E. R. Franchi, and A. Tolstoy

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S36-S36 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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The NRL model for underwater acoustic reverberation incorporates range dependent bathymetry and sound speed profiles. An extended ray theoretic approach for propagation estimates is combined with boundary scattering models to predict, among other measures, reverberation time histories. This paper presents model predictions of ocean basin and margin bottom reverberation for a monostatic source‐receiver geometry at a deep water site in the Northeast Pacific. The reverberation time histories are calculated for a number of radials which are selected to include prominent features such as seamounts, fracture zones, islands, and continental slopes. These time histories are azimuthally convolved with a receiver beam pattern to approximate the total basin reverberation response. The strong backscattered returns from both the continental shelf of North America and large fracture zones are of particular interest. Their strength can be 30 to 50 dB above the normal reverberation decay levels, making them a serious interference, even when located in the beam sidelobes. These predictions provide valuable information for source‐receiver design, signal selection, and pulse repetition rates to be used in future deep sea experiments. [Work supported by Naval Electronic Systems Command, Code 612.]
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An acoustic bottom microprofiler and its application to high‐frequency acoustic scattering (A)

Y. Igarashi and R. L. Allman

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S36-S36 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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Small‐scale bottom roughness data are required for evaluation of high‐frequency scattering models. A shipboard‐launched acoustic microprofiler has been designed for obtaining detailed traces of bottom contours over 6‐ft horizontal line scans in random orientations. The system consists of a 5.75‐MHz, downward‐looking transducer and electronic instrumentation package mounted on a cable‐lowered frame, which is set on the sea floor. A roughness scale of approximately 1 cm–1 m is measured. Estimated vertical and horizontal resolutions are 1 and 8 mm, respectively. Measurements were made in two bottom areas that are nominally flat but have contrasting small‐scale features. Computed roughness height power spectral density functions showed that the magnitude of the spectral functions differed by about 15 dB, but the slopes of the spectra both exhibited a power‐law decay of approximately 2.50. These results are directly applicable to many theoretical scattering formulations with regard to the magnitude and frequency dependence of scattering functions. [Work supported by the Naval Sea Systems Command.]
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Sound‐channel effects on ambient noise spectra (A)

Orest Diachok and Roger Gauss

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S36-S36 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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Possible causes of the observed site dependence of deep ocean noise spectra below 100 Hz are examined through a coherent ambient noise prediction scheme due to Ingentio and Kuperman. The model accounts for propagation from a random distribution of surface dipoles in a range‐independent, compressional sound‐velocity structure; hence data‐theory comparisons were limited to deep ocean abyssal plain environments. A variety of environments from open ocean (with varying wind speeds) to noisy ice‐edge configurations were considered. Results below 100 Hz reveal a frequency‐dependent, sound‐channel‐controlled amplification of the noise‐source spectra which is sensitive to bottom sound‐speed profile/attenuation coefficient and sea‐surface scattering loss. Below 5 Hz the computed amplification accurately accounts for the apparent discrepancy between theoretical and measured noise due to nonlinear interaction of surface waves. Above about 50 Hz the computed noise‐source spectral shapes in the open ocean and at the ice edge were nearly identical, suggesting that the same mechanism (possibly subsurface bubble cavitation) is responsible. Estimated sound‐channel effects on the frequency and wind‐speed dependence of the noise spectra between 5 and 50 Hz, where a different mechanism(s) predominates, will also be presented. [Work supported by 425AR, NAVELEX 612.]
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Modeling the vertical directionality of ambient noise in a range dependent environment using the adiabatic invariant approximation method (A)

John Northrop

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S36-S36 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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The adiabatic invariant approximation (AIA) method of tracing sound rays in a range dependent environment was used in computing the vertical arrival angle of ambient noise at various frequencies and depths. Both surface‐ and bottom‐reflection coefficients were incorporated in the model so that calculations could be performed out to any number of convergence zones. Noise from shipping and storms were treated as monopoles and dipoles, respectively. It is shown how the results can be used to choose the best operating depth for a vertical line array.
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A linear regression model for the wavenumber‐frequency spectrum of the turbulent boundary layer pressure fluctuations (A)

Y. F. Hwang

J. Acoust. Soc. Am. Volume 72, Issue S1, pp. S36-S36 (1982); (1 page)

Online Publication Date: 12 Aug 2005

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The rationale of using a linear regression model for estimating the wavenumber‐frequency spectrum of the turbulent boundary layer pressure fluctuations is presented. The contention is that the wavenumber‐frequency spectra, normalized by the convected ridge value at the respective frequency, are the same for all frequencies if the wavenumber is expressed in terms of a nondimensional wavenumber. The nondimensional wavenumber is the streamwise wavenumber that is normalized by the convected wavenumber at the respective frequency. The normalized wavenumber spectrum is then approximately represented by a set of discrete spectral coefficients. The linear regression model relates the response of a measuring system to these discrete spectral coefficients. The spectral coefficients are determined by least‐square minimization of a set of randomly collected response data.
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