Academic literature on the topic 'Ocean bottom. Reflectance. Underwater acoustics'

In the different parabolic approximations to the reduced wave equation which model acoustic propagation in the ocean, the bottom is usually modeled as an interface and the domain of propagation includes an absorbing layer below the bottom interface. Thus the boundary value problem to be solved has zero boundary conditions at the surface as well as at the bottom boundary. In this paper exact boundary conditions are developed and numerically implemented along the physical bottom boundary if it is horizontal, otherwise, along an artificially placed horizontal computational boundary inside the bottom. These boundary conditions are nonlocal but integrable and can be incorporated in finite difference schemes for the parabolic equations.

In ocean waveguides, the ocean bottom is usually approximated as a half-space. Thus, there exist no reflection waves at the half-space bottom and condition of radiation at infinity should be satisfied. In numerical solutions like parabolic equation methods, the depth domain has to be truncated, which can generate reflection waves from the truncated ocean bottom. To reduce the effect of reflection waves and to simulate an unbounded ocean bottom accurately, an artificial absorbing layer (ABL) was used. As was demonstrated, an ABL meets well the demand of accuracy in sound field calculation. However, both the sea-bottom layer and the artificial absorbing layer are needed to be set quite thick by using an ABL technique. Fortunately, a PML with several wavelengths can keep similar calculation accuracy with an ABL with dozens of wavelengths. In this paper, perfectly matched layer (PML) techniques for three parabolic equation (PE) models RAM, RAMS and a three-dimensional PE model in underwater acoustics are presented. A key technique of PML “complex coordinate stretching” is used to truncate unbounded domains and to simulate infinity radiation conditions instead of the ABL in those models. The numerical results illustrate that the PML technique is of higher efficiency than the ABL technique at truncating the infinity domain with minimal spurious reflections in PE models.

Abstract An unmanned underwater vehicle (UUV) with hyperspectral optical sensors that measure downwelling irradiance and upwelling radiance was deployed over sandy bottoms, sea grass patches, and coral reefs near Lee Stocking Island, Bahamas, during the Coastal Benthic Optical Properties (CoBOP) program of 2000. These deployments occurred during both sunny and cloudy weather. If the rate of irradiance change due to cloud cover is slight, then the inclusion of a variable cloudy-irradiance factor will allow a reasonable estimation of water column absorption. Examination of data from a deployment in May 2000 under cloudy skies shows that the combination of hyperspectral light-field measurements, knowledge of the UUV's position in the water column, and a cloudy-irradiance factor permits consistent estimations of bottom reflectivity to be made from UUV measured reflectances. The spatial distribution of reflectance estimates obtained from a UUV may be useful for validation of airborne ocean color imagery.

Geoacoustic inversion is a very important issue in underwater acoustics, and the inversion method based on bottom reflection loss is a valid technique to invert bottom parameters. This paper describes a Bayesian method for estimating bottom parameters in the deep ocean based on inversion of reflection loss versus angle data which were obtained from an experiment conducted in South China Sea in 2013. The experimental data show that bottom loss depends on frequency. The Bayesian method can be applied in nonlinear inversion problems, and it provides useful indication about the quality of the inversion and parameter sensitivities. The bottom is modeled as a two-layer model, and each layer has constant parameters. The inverted parameters of sediment show a clay feature which is consistent with the core data. Furthermore, the inversion results are used to calculate transmission losses (TLs) along the experiment track which agree well with the direct measurements. Although the inversion results are limited to reveal exact structures of bottom, they are still useful for forecasting propagation losses in this area.

In the applications of the parabolic equation (PE) to underwater sound propagation, the radiation condition as z → ∞ is usually approximated numerically by appending an absorbing layer below the physical ocean bottom and imposing a simple pressure-release boundary condition at the base of the layer. A similar artifice is needed to prevent unphysical reflections from the top of the air layer in the applications of the PE to atmospheric sound propagation. In this paper, we replace this approximate boundary treatment for the standard PE with an exact, nonlocal boundary condition that can be applied along the sea-bottom or upper-atmosphere interface. Moreover, we make use of an exact relationship between the solution ψ of the standard PE and the solution p of the Helmholtz equation to postprocess the ψ field obtained using this nonlocal boundary condition into the p field. The effectiveness of this radiation condition and the a posteriori PE scheme is demonstrated for several examples that typify underwater and outdoor sound propagation in layered media.

A set of experiments were performed in the offshore area off the coasts of Taiwan and three-dimensional (3-D) measurements recorded. The 3-D effect on underwater propagation due to azimuthal variation of bottom topography is studied for the offshore regions southwest of Taiwan, where submarine canyons exist. A 3-D acoustic propagation model, FOR3D, is used to detect the 3-D effect. Computational results show that the 3-D effect is more prominent along the axis of the canyon than across it. Calculations show a very good agreement with field data, which indicate that the 3-D effect exists in this realistic ocean environment.

The ocean ambient noise is one of interference fields of underwater acoustic channel. The design and use of any sonar system are bound to be affected by ocean ambient noise, so to research the spatial correlation characteristics of noise field is of positive significance to improving the performance of sonar system. Only wind-generated noise is considered in most existing ambient noise models. In this case, the noise field is isotropic in horizontal direction. However, due to those influencing factors, like rainfall, ships and windstorm, etc. for a real ocean environment, noise field becomes anisotropic horizontally and the spatial structure of ambient field also changes correspondingly. This paper presents a spatial correlation of the acoustic vector field of anisotropic field by introducing Von Mises probability distribution to describe horizontal directivity. Closed-form expressions are derived which relate the cross-correlation among the sound pressure and three orthogonal components of vibration velocity, besides, the influence of the non-uniformity of noise field on the correlation characteristics of noise vector field was analysed. The model presented in this paper can provide theoretical guidance for the design and application of vector sensors array. Furthermore, the achievement could be applied to front extraction, Green’s function extraction, inversion for ocean bottom parameters, and so on.

Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets. References Nathan D Merchant, Kurt M Fristrup, Mark P Johnson, Peter L Tyack, Matthew J Witt, Philippe Blondel, and Susan E Parks. Measuring acoustic habitats. Methods in Ecology and Evolution, 6(3):257265, 2015. doi:10.1111/2041-210X.12330. Nathan D Merchant, Philippe Blondel, D Tom Dakin, and John Dorocicz. Averaging underwater noise levels for environmental assessment of shipping. The Journal of the Acoustical Society of America, 132(4):EL343EL349, 2012. doi:10.1121/1.4754429. Chi-Fang Chen, Hsiang-Chih Chan, Ray-I Chang, Tswen-Yung Tang, Sen Jan, Chau-Chang Wang, Ruey-Chang Wei, Yiing-Jang Yang, Lien-Siang Chou, Tzay-Chyn Shin, et al. Data demonstrations on physical oceanography and underwater acoustics from the marine cable hosted observatory (macho). In OCEANS, 2012-Yeosu, pages 16. IEEE, 2012. doi:10.1109/OCEANS-Yeosu.2012.6263639. Sauda Sadaf P Yashaswini, Soumya Halagur, Fazil Khan, and Shanta Rangaswamy. A literature survey on ambient noise analysis for underwater acoustic signals. International Journal of Computer Engineering and Sciences, 1(7):19, 2015. doi:10.26472/ijces.v1i7.37. Shuguang Wang and Xiangyang Zeng. Robust underwater noise targets classification using auditory inspired time-frequency analysis. Applied Acoustics, 78:6876, 2014. doi:10.1016/j.apacoust.2013.11.003. LG Weiss and TL Dixon. Wavelet-based denoising of underwater acoustic signals. The Journal of the Acoustical Society of America, 101(1):377383, 1997. doi:10.1121/1.417983. Timothy Alexis Bodisco, Jason D'Netto, Neil Kelson, Jasmine Banks, Ross Hayward, and Tony Parker. Characterising an ecg signal using statistical modelling: a feasibility study. ANZIAM Journal, 55:3246, 2014. doi:10.21914/anziamj.v55i0.7818. José Ribeiro-Fonseca and Luís Correia. Identification of underwater acoustic noise. 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Automatic classification of active sonar echoes for improved target identification. Douglas C Montgomery. Design and analysis of experiments. John wiley and sons, 2017. doi:10.1002/9781118147634. G Taguchi. Off-line and on-line quality control systems. In Proceedings of International Conference on Quality Control, 1978. Sheng-Ju Wu, Sheau-Wen Shiah, and Wei-Lung Yu. Parametric analysis of proton exchange membrane fuel cell performance by using the taguchi method and a neural network. Renewable Energy, 34(1):135144, 2009. doi:10.1016/j.renene.2008.03.006. Genichi Taguchi. Introduction to quality engineering: designing quality into products and processes. Technical report, 1986. doi:10.1002/qre.4680040216. Richard Horvath, Gyula Matyasi, and Agota Dregelyi-Kiss. Optimization of machining parameters for fine turning operations based on the response surface method. ANZIAM Journal, 55:250265, 2014. doi:10.21914/anziamj.v55i0.7865. Chuan-Tien Li, Sheng-Ju Wu, and Wei-Lung Yu. Parameter design on the multi-objectives of pem fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms. International Journal of Hydrogen Energy, 39(9):45024515, 2014. doi:10.1016/j.ijhydene.2014.01.034. Antoine Guisan, Thomas C Edwards Jr, and Trevor Hastie. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2-3):89100, 2002. doi:10.1016/S0304-3800(02)00204-1. Sheng Chen, Colin FN Cowan, and Peter M Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on neural networks, 2(2):302309, 1991. doi:10.1109/72.80341. Howard Demuth and Mark Beale. Neural network toolbox for use with matlab-user's guide verion 4.0. 1993. Janice Gaffney, Charles Pearce, and David Green. Binary versus real coding for genetic algorithms: A false dichotomy? ANZIAM Journal, 51:347359, 2010. doi:10.21914/anziamj.v51i0.2776. 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The shear-wave (S-wave) structures of shallow marine sediments are important for offshore geotechnical studies, deep crustal S-wave imaging, multicomponent seismic exploration, and underwater acoustics studies. We have applied the multicomponent Scholte-wave analysis technique to an active-source shallow marine seismic profile in the East China Sea. Scholte waves have been excited by shots from a 5450 inch3 air-gun array and their recordings have been conducted at the seafloor using ocean bottom nodes (OBNs). First, we extract the common-receiver gathers (CRGs) and correct for the time drift simultaneously using a forward and inverse fast Fourier transform resampling algorithm. Three CRGs of seismic sensors are used for Scholte-wave analysis. Raw sensor CRGs are rotated to the inline, crossline, and vertical coordinate system. The rotated tilt and roll angle are directed using the inner electric compass log value, and the shot inline azimuth is estimated using the particle motion method. Then, the velocity spectra are calculated from the inline and vertical components using the phase-shift method. Higher Scholte-wave modes dominate on the horizontal components, whereas the stronger fundamental mode dominates on the vertical components. The multicomponent velocity spectrum stacking method is adopted to produce the final dispersion energy image. Up to four modes of dispersion curves are retrieved within the 1.1–4.3 Hz frequency band. The multimode dispersion curve inversion is constructed for imaging the shallow sediments. The results suggest a low [Formula: see text] of 180–650 m/s and few lateral variations within the top 0.5 km of shallow marine sediments in the East China Sea. This model can provide an important reference for offshore geotechnical investigations, especially for OBN multicomponent seismic exploration data processing. The use of OBNs has high feasibility in [Formula: see text] imaging for shallow marine sediments when combined with the Scholte-wave dispersion-curve inversion.

Accurate modeling of acoustic propagation in the ocean waveguide is important to SONAR-performance prediction, and requires, particularly in shallow water environments, characterizing the bottom reflection loss with a precision that databank-based modeling cannot achieve. Recent advances in the technology of autonomous underwater vehicles (AUV) make it possible to envision a survey system for seabed characterization composed of a short array mounted on a small AUV. The bottom power reflection coefficient (and the related reflection loss) can be estimated passively by beamforming the naturally occurring marine ambient-noise acoustic field recorded by a vertical line array of hydrophones. However, the reduced array lengths required by small AUV deployment can hinder the process, due to the inherently poor angular resolution. In this dissertation, original data-processing techniques are presented which, by introducing into the processing chain knowledge derived from physics, can improve the performance of short arrays in this particular task. Particularly, the analysis of a model of the ambient-noise spatial coherence function leads to the development of a new proof of the result at the basis of the bottom reflection-loss estimation technique. The proof highlights some shortcomings inherent in the beamforming operation so far used in this technique. A different algorithm is then proposed, which removes the problem achieving improved performance. Furthermore, another technique is presented that uses data from higher frequencies to estimate the noise spatial coherence function at a lower frequency, for sensor spacing values beyond the physical length of the array. By "synthesizing" a longer array, the angular resolution of the bottom-loss estimate can be improved, often making use of data at frequencies above the array design frequency, otherwise not utilized for beamforming. The proposed algorithms are demonstrated both in simulation and on real data acquired during several experimental campaigns.

Research on the propagation of acoustic waves in ocean bottom sediment is of interest for active sonar applications such as target detection and remote sensing. Currently, all seabed scattering models available in the literature are based on the full solution of the wave equation, which sometimes leads to mathematically intractable problems. In the electromagnetics community, an alternative formulation that overcomes some of this complexity is radiative transfer theory, which has established itself as an important technique for remote sensing. In this work, radiative transfer (RT) theory is proposed for the first time as a tool for the study of seabed acoustic scattering. The focus of this work is the development of a complete model for the interaction of acoustic energy with water-saturated sediments. The general geometry considered in this study consists of multiple elastic layers containing random distributions of inhomogeneities. The accuracy of the proposed model is assessed by rigorous experimental work, with data collected from random media in which acoustic properties such as the concentration and size of scatterers, background material, and the presence of elastic boundaries are controlled parameters. First, the ultrasound RT model is implemented for layers of finite thickness. The range of applicability of the proposed model is then illustrated using scaled experiments conducted at the Northwest Electromagnetics and Acoustics Research Laboratory (NEAR-Lab). Next, the model is applied to field data collected in a region with gassy sediments and compared to the formulation originally used to explain these data. Finally, insight into the emerging area of study of the time-dependent RT formulation is presented, and its role in the representation of finite broadband pulses is discussed.

Thesis (M.S. in Engineering Acoustics)--Naval Postgraduate School, December 1990.
Thesis Advisor(s): Coppens, Alan B. ; Sanders, James V. "December 1990." Description based on title screen as viewed on March 31, 2010. DTIC Identifier(s): Sound Pressure, Sound Transmission, Transmission Loss, Parabolic Equation Models, Computerized Simulation, Underwater Acoustics, Acoustic Velocity, Ocean Bottom, Ocean Models, Theses. Author(s) subject terms: Image Method, Parabolic Equation Model, Wedge-shaped Ocean. Includes bibliographical references (p. 37). Also available in print.

The seafloor properties are of high importance for many applications such as marine biology, oil and gas exploration, laying cables, dredging operations and off-shore construction. Several approaches exist to classify the properties of the seabed. These include taking direct samples of the seabed (e.g., coring), however, these methods are costly and slow. Underwater acoustic remote sensing techniques are of interest because they are lower cost and faster. The information about the seabed properties can be extracted by studying the energy of single beam echo sounders (SBES). This can be done by either phenomenological or numerical methods [1], [2]. This research investigates a numerical, model-data fitting method using a high frequency backscattering model developed by Jackson et al [3]. In this "inversion modeling" method, the matching process between the model and average echo envelope provides information about the sediment parameters, namely the sediment mean grain size (Mz) as the indicator of the seabed type, spectral parameter (W2) as the indicator of seabed roughness and normalized sediment volume parameter σ2 as the indicator of the scattering due to sediment inhomogeneities.

Thesis (Ph. D. in Applied Ocean Sciences)--Joint Program in Applied Ocean Physics and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering; and the Woods Hole Oceanographic Institution), February 2002.
Includes bibliographical references (leaves 161-170).
Sound propagation in shallow water is highly dependent on the interaction of the sound field with the bottom. In order to fully understand this problem, it is necessary to obtain reliable estimates of bottom geoacoustic properties that can be used in acoustic propagation codes. In this thesis, perturbative inversion methods and exact inverse methods are discussed as a means for inferring geoacoustic properties of the bottom. For each of these methods, the input data to the inversion is the horizontal wavenumber spectrum of a point-source acoustic field. The main thrust of the thesis work concerns extracting horizontal wavenumber content for fully three-dimensionally varying waveguide environments. In this context, a high-resolution autoregressive (AR) spectral estimator was applied to determine wavenumber content for short aperture data. As part of this work, the AR estimator was examined for its ability to detect discrete wavenumbers in the presence of noise and also to resolve closely spaced wavenumbers for short aperture data. As part of a geoacoustic inversion workshop, the estimator was applied to extract horizontal wavenumber content for synthetic pressure field data with range-varying geoacoustic properties in the sediment. The resulting wavenumber content was used as input data to a perturbative inverse algorithm to determine the sound speed profile in the sediment. It was shown using the high-resolution wavenumber estimator that both the shape and location of the range-variability in the sediment could be determined.
(cont.) The estimator was also applied to determine wavenumbers for synthetic data where the water column sound speed contained temporal variations due to the presence of internal waves. It was shown that reliable estimates of horizontal wavenumbers could be obtained that are consistent with the boundary conditions of the waveguide. The Modal Mapping Experiment (MOMAX), an experimental method for measuring the full spatial variability of a propagating sound field and its corresponding modal content in two-dimensions, is also discussed. The AR estimator is applied to extract modal content from the real data and interpreted with respect to source/receiver motion and geometry. For a moving source, it is shown that the wavenumber content is Doppler shifted. A method is then described that allows the direct measure of modal group velocities from Doppler shifted wavenumber spectra. Finally, numerical studies are presented addressing the practical issues associated with using MOMAX type data in the exact inversion method of Gelfand-Levitan.
by Kyle M. Becker.
Ph.D.

This chapter deals with the mathematics of ocean acoustics. A number of environmental factors affect the transmission of sound in the ocean, including the depth and configuration of the bottom, the sound velocity structure within the ocean, and the shape of the ocean surface. The depths in the ocean are distributed in a peculiar manner, and the solution of underwater-sound problems may be grouped into two categories that differ mainly in terms of dimension: the average depths of water for deep-water transmission are 10,000 to 20,000 feet, whereas those for shallow-water transmission are less than 300 feet. The chapter first provides an overview of ocean acoustic waveguides before discussing one-dimensional waves in an inhomogeneous medium. It also considers a mathematical model of acoustic wave propagation in a stratified fluid and concludes with an analysis of the one-dimensional time-independent Schrödinger equation for solving the potential well problem.