A time series analysis of sound propagation in a strongly multipath shallow water environment with an adiabatic normal mode approach

1996 ◽  
Vol 21 (1) ◽  
pp. 1-13 ◽  
Author(s):  
D.P. Knobles ◽  
R.A. Koch
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Shallow Water ◽  
Normal Mode ◽  
Sound Propagation ◽  
Water Environment ◽  
Series Analysis ◽  
Shallow Water Environment

Time series analysis of propagation in range‐dependent shallow‐water environment

1998 ◽  
Vol 103 (5) ◽  
pp. 2856-2856
Author(s):  
Finn B. Jensen ◽  
Francesco Bini‐Verona ◽  
Peter L. Nielsen ◽  
Peter Gerstoft
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Shallow Water ◽  
Water Environment ◽  
Series Analysis ◽  
Shallow Water Environment

scholarly journals Underwater Sound Propagation Modeling in a Complex Shallow Water Environment

2021 ◽  
Vol 8 ◽  
Author(s):  
Tiago C. A. Oliveira ◽  
Ying-Tsong Lin ◽  
Michael B. Porter
Keyword(s):  
Shallow Water ◽  
Normal Mode ◽  
Sound Propagation ◽  
Long Island ◽  
Water Environment ◽  
Long Island Sound ◽  
Underwater Sound ◽  
Shallow Water Environment ◽  
3D Effects ◽  
Island Sound

Three-dimensional (3D) effects can profoundly influence underwater sound propagation in shallow-water environments, hence, affecting the underwater soundscape. Various geological features and coastal oceanographic processes can cause horizontal reflection, refraction, and diffraction of underwater sound. In this work, the ability of a parabolic equation (PE) model to simulate sound propagation in the extremely complicated shallow water environment of Long Island Sound (United States east coast) is investigated. First, the 2D and 3D versions of the PE model are compared with state-of-the-art normal mode and beam tracing models for two idealized cases representing the local environment in the Sound: (i) a 2D 50-m flat bottom and (ii) a 3D shallow water wedge. After that, the PE model is utilized to model sound propagation in three realistic local scenarios in the Sound. Frequencies of 500 and 1500 Hz are considered in all the simulations. In general, transmission loss (TL) results provided by the PE, normal mode and beam tracing models tend to agree with each other. Differences found emerge with (1) increasing the bathymetry complexity, (2) expanding the propagation range, and (3) approaching the limits of model applicability. The TL results from 3D PE simulations indicate that sound propagating along sand bars can experience significant 3D effects. Indeed, for the complex shallow bathymetry found in some areas of Long Island Sound, it is challenging for the models to track the interference effects in the sound pattern. Results emphasize that when choosing an underwater sound propagation model for practical applications in a complex shallow-water environment, a compromise will be made between the numerical model accuracy, computational time, and validity.

Modal time-series structure in a shallow-water environment [Hudson Canyon region]

1998 ◽  
Vol 23 (3) ◽  
pp. 188-202 ◽  
Author(s):  
D.P. Knobles ◽  
E.K. Westwood ◽  
J.E. Le Mond
Keyword(s):  
Time Series ◽  
Shallow Water ◽  
Water Environment ◽  
Shallow Water Environment

The Development of Predictability Measurement Method for Winter Monsoon Forecasts in Thailand

2017 ◽  
Vol 866 ◽  
pp. 160-163
Author(s):  
Sunisa Saiuparad
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Shallow Water ◽  
Measurement Method ◽  
Climate Model ◽  
Global Climate Model ◽  
Winter Monsoon ◽  
Water Model ◽  
Shallow Water Model ◽  
Series Analysis

The forecasting method is important because it can predict phenomena in the future. The accuracy of the forecast depends on the model and the initial conditions. In addition, the predictability measurement method is important can be check the accurate of forecasts. In this research is winter monsoon forecasts in Thailand by the shallow water model. The data from The Bjerknes Centre for Climate Research (BCCR), University of Bergen, Norway. The global climate model is Bergen Climate Model (BCM) Version 2.0 (BCCR-BCM2.0) of the Intergovernmental Panel on Climate Change (IPCC) is used. The Lyapunov exponent (LE) is the predictability measurement method for verify the efficiency of model and establish the new predictability measurement method by the time series analysis. The result to show that the new predictability measurement method by the time series analysis can be measure the efficiency of the winter monsoon forecasts in Thailand by the shallow water model for December 2015 to December 2056 are suitable.

Influences Analysis of Environmental Parameters on Sound Propagation in Shallow Water

2014 ◽  
Vol 556-562 ◽  
pp. 4815-4819
Author(s):  
Shahabuddin Shaikh ◽  
Yi Wang Huang
Keyword(s):  
Shallow Water ◽  
Sound Speed ◽  
Sound Propagation ◽  
Transmission Loss ◽  
Water Environment ◽  
Environmental Parameters ◽  
Sea Surface ◽  
Water Medium ◽  
Sea Bottom ◽  
Shallow Water Environment

The objectives of this paper are to analyze the effectiveness of parameters on sound propagation in a shallow-water environment. The procedure for calculation of transmission loss is only the method to analyze the influence of environmental parameters. The normal mode approach is carried out for the calculation of transmission loss. And it is conducted in the range independent environment Transmission loss for sound propagation in shallow water depends upon many natural variables such as sea surface, water medium, and sea bottom. Analyses are finalized on the results obtained by considering two types of sound channels. The results indicated that acoustic transmission loss in a shallow-water environment is dependent on the source & receiver depths, sea surface, sound speed profile (SSP) in water, sound speed in bottom, density of water & bottom, propagation range and frequency. It is necessary to mention that better transmission was found during the sound velocity increases with depth; whereas the poor transmission occurred in negative gradient channel.

Coherent reverberation model based on adiabatic normal mode theory in a range dependent shallow water environment

2010 ◽  
Author(s):  
Zhenglin Li ◽  
Renhe Zhang ◽  
Fenghua Li ◽  
Jeffrey Simmen ◽  
Ellen S. Livingston ◽  
...  
Keyword(s):  
Shallow Water ◽  
Normal Mode ◽  
Water Environment ◽  
Mode Theory ◽  
Model Based ◽  
Shallow Water Environment ◽  
Normal Mode Theory

scholarly journals Development and Application of a Pre-Corrected Fast Fourier Transform Accelerated Multi-Layer Boundary Element Method for the Simulation of Shallow Water Acoustic Propagation

2020 ◽  
Vol 10 (7) ◽  
pp. 2393
Author(s):  
Chengxi Li ◽  
Jijian Lian
Keyword(s):  
Boundary Element Method ◽  
Shallow Water ◽  
Boundary Element ◽  
Sound Propagation ◽  
Water Environment ◽  
Equation System ◽  
Linear Equation System ◽  
Shallow Water Environment ◽  
Classical Boundary ◽  
Element Method

Because of the complexities associated with the domain geometry and environments, accurate prediction of acoustics propagation and scattering in realistic shallow water environments by direct numerical simulation is challenging. Based on the pre-corrected Fast Fourier Transform (PFFT) method, we accelerated the classical boundary element method (BEM) to predict the acoustic propagation in a multi-layer shallow water environment. The classical boundary element method formulate the acoustics propagation problem as a linear equation system in the form of [A]{x}={b}, where [A] is an N×N dense matrix composed of influence coefficients. Solving such linear equation system requires O(N2/N3) computational cost for iterative/direct methods. The developed method, PFFT-BEM, can effectively reduce the computational efforts for direct numerical simulations from O(N2~3) to O(Nlog N), where N is the total number of boundary unknowns. To numerically simulate the sound propagation in a shallow water environment, we applied the first-order non-reflecting boundary condition in the truncated numerical domain boundary to eliminate the errors due to reflected waves. Multi-layer coupled formulation was used to include the environment inhomogeneity in PFFT-BEM. Through multiple convergence tests on the number of layers and elements, we validated and quantified the accuracy of PFFT-BEM. To demonstrate the usefulness and capability of the developed PFFT-BEM, we simulated three-dimensional (3D) underwater sound propagation through 3D geometries to check the efficacy of the established classical method: the 3D Parabolic equation model. Finally, PFFT-BEM was employed to simulate sound propagation through a complex multi-layer shallow water environment with internal waves. The “3D+T” results obtained by PFFT-BEM compared well with the physical test, thereby proving the capability and correctness of this method.

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