Ambient Noise Vertical Directionality and Reflection Loss Estimates for a Shallow Tropical Site with a Fast Sound Speed Bottom

2018 ◽  
Vol 41 (5) ◽  
pp. 427-438
Author(s):  
M. C. Sanjana ◽  
G. Raguraman ◽  
G. Latha

Naturally generated ambient noise in the ocean is created by breaking waves, spray and precipitation. Each of these mechanisms produces a pulse of sound that propagates down into the depths of the ocean, and the superposition of all such pulses from across the whole sea surface constitutes the ambient noise field. Since the noise is a stochastic phenomenon, its properties are described in terms of statistical quantities, the most useful being the power spectral density at a point and the cross-spectral density between two points in the field. If these second-order statistical measures are independent of absolute position, the noise field is said to be spatially homogeneous. In the rare case of an isovelocity, deep ocean, the noise field at depths greater than a wavelength or so beneath the surface is spatially homogeneous, consisting of a random superposition of plane waves. A non-uniform sound speed profile, however, introduces wave-front curvature which modifies the situation significantly. the noise exhibits strong spatial homogeneity over length scales that are comparable with the apertures of typical acoustic arrays. Apart from the implications with regard to array performance, this is important in connection with certain aspects of acoustical oceanography, whereby information on the oceanographic environment is extracted from the noise field (Buckingham et al. 1992). Such information is accessible only if the structure of the noise field is well understood. The problem lies in determining the spatial and spectral properties of the noise in a profile. Fundamental to the noise analysis is the Green’s function for the channel, which characterizes the propagation conditions; and yet for most non-uniform sound speed profiles the analysis of the Green's function is intractable. However, there is one profile, designated the inverse-square profile, for which a complete, exact solution for the field has been developed (Buckingham 1991). The inverse-square profile is monotonic increasing with depth, giving rise to upward refractive propagation. Such a profile is found in several ocean environments: the polar oceans, where the temperature and hence the sound speed show a minimum at the surface; the mixed surface layer, extending to a depth of order 100 m in the open ocean; and the ocean-surface bubble layer, occupying the first ten metres or so beneath the surface. An analysis of the noise field in the presence of an inverse square profile, based on the solution for the Green’s function, shows that the cross-spectral density of the noise in the vertical consists of three components: a normal mode sum, representing noise originating largely in distant sources; a direct path contribution, from sources that are more or less overhead; and a near-surface term that is negligible at depths greater than a wavelength. In the theoretical noise spectrum , the normal mode and direct path components are prominent, dominating, respectively, at low and high frequencies. The cross-over frequency depends on the parameters of the profile and attenuation in the medium, but for polar oceans is in the region of several hundred hertz. At a much lower frequency, around 10 Hz, where the polar profile ceases to support normal mode propagation, a minimum appears in the theoretical spectrum . This is the result of a very rapid fall off in the normal mode component of the noise and a slow rise of the direct path component with decreasing frequency. Each of the three components of the vertical cross-spectral density exhibits strong spatial inhomogeneity. This is exemplified by the dramatic dependence of the cross-spectrum on both the mean depth of the sensors and frequency. Although such behaviour adds complexity to the structure of the noise field, this could be advantageous since it allows the possibility of performing inversions on noise cross-spectral data to determine properties of the medium. Recent measurements of low-frequency (50-2000 Hz) and very low-frequency (5-200 Hz) ambient noise spectra in the marginal ice zone of the Greenland Sea, where the sound speed profile is of the inverse-square form, have been compared with the predictions of the new noise theory. There is evidence in the measured spectra that both the normal mode and direct path components of the noise are present with the predicted relative levels. A minimum around 10 Hz is a ubiquitous feature of the VLF spectra, and the LF spectra show a change of slope close to 400 Hz, both of which are in accord with the theory. Along the ice edge a highly non-uniform (spatial) distribution of energetic sources is known to be present, whose effects in the observed spectra are consistent with arguments developed from the inverse-square noise analysis.


2014 ◽  
Vol 577 ◽  
pp. 1207-1210
Author(s):  
Chun Xia Meng ◽  
Hao Mu ◽  
Gui Juan Li

The vertical directivity characteristic of ambient noise is one inherent characteristic of the ocean in shallow water. And it includes the information of guide’s acoustic characteristic information. The marine guide is composed of sea water; seabed and surface boundary, there into, the acoustic parameters of seabed are hardly obtained exactly. In this paper, the model of vertical directivity for ambient noise is established. Based on the ray theory of sound propagation, the influence of guide’s acoustic parameters which include sound speed, density and attenuation coefficient on vertical directivity of marine ambient noise is simulated. The results are propitious to analysis and command the characteristics of ambient noise, and valuable to accelerate the exertion of acoustic equipment performance.


2019 ◽  
Vol 283 ◽  
pp. 08004
Author(s):  
He Li ◽  
Xiniyi Guo ◽  
Li Ma ◽  
Guoli Song

When solving traditional underwater problems, the boundary condition is always used to calculate the sound field. In practice, however, it is hard to get the boundary conditions of the seabed. So geoacoustics inversion is needed to acquire the parameters of the seabed. In this paper, a method estimating seabed parameters by using the spatial characteristics of ocean ambient noise is demonstrated without using matched-field processing. For the reason of the limit of the resolution of conventional beamforming (CBF), a method of synthetic array processing (SAP) is used because of some characters of cross-spectrum density matrix (CSDM). The result shows that the method of synthetic array processing enhanced the resolution of critical angle to some degree. By comparing the true bottomloss calculated by OASR, the result of traditional beamforming and the synthetic array processing, the result of synthetic array processing is closer to the true bottomloss than the result of traditional beamforming. After ensuring a range of critical angle, the sound speed of the seabed can be estimated by using Snell law. And then, an experimental data collected in Qingdao, China, 2016 is used to prove the validity of the method of synthetic array processing and estimate the local seabed parameters.


2020 ◽  
Author(s):  
Najeem Shajahan ◽  
David Barclay

<p>Ambient noise measurements have been widely used to estimate environmental information such as water column sound speed, pH, seabed properties, and wind speed. In this study, 30 days of ambient noise data recorded on two vertically oriented hydrophones deployed near Alvin canyon on the New England shelf break were used to estimate the ocean mixed layer depth (MLD). The vertical noise coherence was computed and compared to a wave-number integral noise model comprised of a two-segment piecewise linear summer sound speed profile in a shallow water waveguide. Measurements of noise and sound speed profiles, together with a wavenumber integral ambient noise model were used to calculate the mixed layer thickness. Noise model results showed variations in the first zero-crossing frequency, which was in accordance with the semi-diurnal variability of the MLD. MLD was determined by matching the zero-crossing frequency of the real part of measured coherence with the model results for the entire one-month period. The comparison of the estimated MLD using ambient noise showed good agreement with the measured MLD from the temperature sensors.</p>


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