scholarly journals Time frequency spectrum of atmospheric turbulence and sweeping hypothesis

2011 ◽  
Vol 1 (2) ◽  
pp. 022005 ◽  
Author(s):  
Xin Zhao
Keyword(s):  
Frequency Spectrum ◽  
Atmospheric Turbulence ◽  
Time Frequency

Improved EEMD in the Application of Signal Singularity Detection

2012 ◽  
Vol 518-523 ◽  
pp. 3847-3851
Author(s):  
Mei Jun Zhang ◽  
Chuang Wang ◽  
Hao Chen ◽  
Qun Zhang Tu
Keyword(s):  
Frequency Spectrum ◽  
Original Data ◽  
Singularity Detection ◽  
Time Frequency ◽  
Local Characteristics ◽  
Cross Terms ◽  
Marginal Spectrum ◽  
Frequency Components ◽  
Detection Signal ◽  
Improved Eemd

In order to solve the endpoint effect and modal aliasing phenomenon in EMD and EEMD,Improved EEMD is put forward, and the application in signal singularity detection is researched in this paper. The improved EEMD will signal drops down into a series of different IMF to highlight the different local characteristics of original data, and then calculate Hilbert marginal spectrum and time-frequency spectrum to determine the frequency of these mutations and mutations position. To compared with FT, STFT, WVD,WT, EMD and EEMD etc, No cross-terms and no false IMF components are produced in the Hilbert time-frequency spectrum of the improved EEMD. Different frequency components and frequency mutations position are clearly distinguished at the same time. The Hilbert time-frequency spectrum of the improved EEMD has more superior detection signal singularity ability.

Non-Gaussian Wave Groups Generated in an Offshore Wave Basin

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Z. Cherneva ◽  
C. Guedes Soares
Keyword(s):  
Gaussian Process ◽  
Frequency Spectrum ◽  
Local Spectrum ◽  
Wave Groups ◽  
High Wave ◽  
Time Frequency ◽  
Wave Basin ◽  
The Mean ◽  
Non Gaussian ◽  
Experimental Runs

The main goal of the present paper is to study the differences of the descriptors of the wave groups in the nonlinear case in comparison with the same parameters for a Gaussian process. The data analyzed are from a deep water basin of Marintek. They consist of sequence of five identical independent experimental runs of unidirectional waves measured at ten fixed points situated in different distances from the wave maker. Each series contain about 1800 waves. Thus the statistics collected from a given gauge comprise about 9000 waves combined in a number of wave groups. Because the series describe a process significantly different from the Gaussian one, an upper and lower envelopes are introduced as lines which connect the peaks of the crests and the lower points of the troughs respectively. Spline functions are applied to calculate these envelopes. Then, the mean high run and mean group length are estimated for different levels, their ensemble average over five experimental runs is found for every gauge and is compared with the results of the theory of Gaussian process. It is found that the values of the mean time intervals of the groups correlate with coefficient of kurtosis of the process. It is determined also that mean group length is shorter and the mean high run is larger for the nonlinear wave groups in comparison with the Gaussian wave groups. The modification of wave groups in space and time is investigated in the work as well. Wigner time-frequency spectrum with Choi-Williams kernel is applied to describe the process of entire modulation and demodulation of the groups. It is found that before formation of the high wave a wave down-shifting takes place. At this stage the local spectrum is relatively narrow and the group shrinks continuously. Close to the focus the time-frequency spectrum is very wide and the group has a triangle form. Further the high wave breaks and the wave group acquires the form of “three sisters.” The transform of the group continues by its disintegration, the local spectrum stands narrow and an up-shifting is observed.

Leak location in gas pipelines using cross-time–frequency spectrum of leakage-induced acoustic vibrations

2014 ◽  
Vol 333 (17) ◽  
pp. 3889-3903 ◽  
Author(s):  
Shuaiyong Li ◽  
Yumei Wen ◽  
Ping Li ◽  
Jin Yang ◽  
Xiaoxuan Dong ◽  
...  
Keyword(s):  
Frequency Spectrum ◽  
Acoustic Vibrations ◽  
Gas Pipelines ◽  
Time Frequency ◽  
Leak Location

scholarly journals Seismic time-frequency spectral decomposition by matching pursuit

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. V13-V20 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Frequency Spectrum ◽  
Acoustic Waves ◽  
Large Scale ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Gas Reservoir ◽  
Adaptive Wavelet ◽  
Redundant Dictionary ◽  
Time Frequency ◽  
Wavelet Selection

A seismic trace may be decomposed into a series of wavelets that match their time-frequency signature by using a matching pursuit algorithm, an iterative procedure of wavelet selection among a large and redundant dictionary. For reflection seismic signals, the Morlet wavelet may be employed, because it can represent quantitatively the energy attenuation and velocity dispersion of acoustic waves propagating through porous media. The efficiency of an adaptive wavelet selection is improved by making first a preliminary estimate and then a localized refining search, whereas complex-trace attributes and derived analytical expressions are also used in various stages. For a constituent wavelet, the scale is an important adaptive parameter that controls the width of wavelet in time and the bandwidth of the frequency spectrum. After matching pursuit decomposition, deleting wavelets with either very small or very large scale values can suppress spikes and sinusoid functions effectively from the time-frequency spectrum. This time-frequency spectrum may be used in turn for lithological analysis—for instance, detection of a gas reservoir. Investigation shows that the low-frequency shadow associated with a carbonate gas reservoir still exists, even high-frequency amplitudes are compensated by inverse-[Formula: see text] filtering.

Improving the resolution of seismic traces based on the secondary time–frequency spectrum

2017 ◽  
Vol 14 (2) ◽  
pp. 236-246 ◽  
Author(s):  
De-Ying Wang ◽  
Jian-Ping Huang ◽  
Xue Kong ◽  
Zhen-Chun Li ◽  
Jiao Wang
Keyword(s):  
Frequency Spectrum ◽  
Time Frequency ◽  
Secondary Time
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