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.

Depth-integrated equation for hydro-acoustic waves with bottom damping

2015 ◽  
Vol 766 ◽  
Author(s):  
Ali Abdolali ◽  
James T. Kirby ◽  
Giorgio Bellotti
Keyword(s):  
Acoustic Waves ◽  
Large Scale ◽  
Model Equation ◽  
Low Frequency ◽  
3D Models ◽  
Sediment Layer ◽  
Sea Bed ◽  
Weakly Compressible ◽  
Acoustic Wave Generation

AbstractWe present a depth-integrated equation for the mechanics of generation, propagation and dissipation of low-frequency hydro-acoustic waves due to sudden bottom displacement in a weakly compressible ocean overlying a weakly compressible viscous sediment layer. The model is validated against a full 3D computational model. Physical properties of these waves are studied and compared with those for waves over a rigid sea bed, revealing changes in the frequency spectrum and modal peaks. The resulting model equation can be used for numerical prediction in large-scale domains, overcoming the computational difficulties of 3D models while taking into account the role of bottom dissipation on hydro-acoustic wave generation and propagation.

scholarly journals Analysis of Vibroacoustic Modulations for Crack Detection: A Time-Frequency Approach Based on Zhao-Atlas-Marks Distribution

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Trochidis ◽  
L. Hadjileontiadis ◽  
K. Zacharias
Keyword(s):  
Acoustic Waves ◽  
Crack Detection ◽  
Damage Index ◽  
Low Frequency ◽  
Crack Size ◽  
Modulation Amplitude ◽  
Nonlinear Method ◽  
Time Frequency ◽  
Low Frequency Vibration ◽  
Acoustic Modulation

The vibro-acoustic modulation (VAM) technique is probably the most widely used nonlinear method for crack detection. The VAM method is based on the effect of modulation of high-frequency acoustic waves by a low-frequency vibration. The intensity of the modulation is related to the severity of the damage and has been used so far as a damage index. The damage index simply based on the amplitude of the first side bands in the spectral domain often leads to controversial results about the severity of the damage. In this work, the nonlinear characteristics of the vibro-modulation were systematically investigated by employing time-frequency analysis based on the Zhao-Atlas-Marks (ZAM) distribution. The results of the analysis show that the amplitude of the sideband components is modulated by the low frequency vibration and the modulation amplitude depends on the size of the crack. Based on the obtained results, a new damage index was defined in relation to the strength of the modulation. The new damage index is more sensitive and robust and correlates better with crack size compared to the index based on the amplitude of the sidebands.

Shale Gas Prediction Technique Based on the Matching Pursuit Algorithm

2013 ◽  
Vol 807-809 ◽  
pp. 2249-2256
Author(s):  
Zhong Yu Duan ◽  
Run Qiu Wang ◽  
Xiao Peng Liu ◽  
Qing Shan Pu ◽  
Liang Tong Fu
Keyword(s):  
Shale Gas ◽  
Seismic Data ◽  
Matching Pursuit ◽  
Seismic Signal ◽  
Gas Content ◽  
Critical Parameters ◽  
Signal Spectrum ◽  
Gas Reservoir ◽  
Time Frequency ◽  
Shale Gas Reservoir

Shale gas reservoir characterizes source bed being its bearing reservoir, no gaswater interface, low porosity and low permeability. So the exploration and production procedure of shale gas is much different from traditional gas. How to detect the gas content of the shale formation is the key problem to exploit the shale gas. An algorithm to calculate seismic signal spectrum indicated the decay of high frequency is proposed. This algorithm adopt matching pursuit spectrum decomposing method to do high accuracy time frequency analysis on seismic data under the condition that the nonhomogeneity size of geologic body is not much larger than the wavelength of seismic wave and the seismic data is processed with high resolution amplitude maintained method. Do geostatistic analysis on energy ratio of the objective interval which gets from the frequency spectrum of the interval. Then the distribution character of shale gas in the shale reservoir can be got. This technique is an effective geophysical method to identify and evaluate the shale gas reservoir. It can provide critical parameters to explore and develop shale gas. Gas content detection gets effective result by using this technique in Block PengShui of Sichuan Basin.

scholarly journals Multichannel matching pursuit for seismic trace decomposition

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. V61-V66 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
High Resolution ◽  
Correlation Coefficient ◽  
Frequency Spectrum ◽  
Matching Pursuit ◽  
Data Noise ◽  
Spatial Continuity ◽  
Time Frequency ◽  
Uniqueness Of The Solution ◽  
Single Trace

The technique of matching pursuit can adaptively decompose a seismic trace into a series of wavelets. However, the solution is not unique and is also strongly affected by data noise. Multichannel matching pursuit (MCMP), exploiting lateral coherence as a constraint, might improve the uniqueness of the solution. It extracts a constituent wavelet that has an optimal correlation coefficient to neighboring traces, instead of to a single trace only. According to linearity theory, a wavelet shared by neighboring traces is the best match to the average of multiple traces, and therefore it might effectively suppress the data noise and stabilize the performance. It is found that the MCMP scheme greatly improves spatial continuity in decomposition and can generate a plausible time-frequency spectrum with high resolution for reservoir detection.

scholarly journals Fast Matching Pursuit with Multi-Gabor Dictionaries

2021 ◽  
Vol 47 (3) ◽  
pp. 1-20
Author(s):  
Zdeněk Průůa ◽  
Nicki Holighaus ◽  
Peter Balazs
Keyword(s):  
Frequency Shift ◽  
Matching Pursuit ◽  
Hard Problem ◽  
Round Trip ◽  
Redundant Dictionary ◽  
Time Frequency ◽  
Acceleration Technique ◽  
Np Hard Problem ◽  
Matching Pursuit Algorithm ◽  
Gabor Dictionary

Finding the best K -sparse approximation of a signal in a redundant dictionary is an NP-hard problem. Suboptimal greedy matching pursuit algorithms are generally used for this task. In this work, we present an acceleration technique and an implementation of the matching pursuit algorithm acting on a multi-Gabor dictionary, i.e., a concatenation of several Gabor-type time-frequency dictionaries, each of which consists of translations and modulations of a possibly different window and time and frequency shift parameters. The technique is based on pre-computing and thresholding inner products between atoms and on updating the residual directly in the coefficient domain, i.e., without the round-trip to the signal domain. Since the proposed acceleration technique involves an approximate update step, we provide theoretical and experimental results illustrating the convergence of the resulting algorithm. The implementation is written in C (compatible with C99 and C++11), and we also provide Matlab and GNU Octave interfaces. For some settings, the implementation is up to 70 times faster than the standard Matching Pursuit Toolkit.

Matching pursuit-based sparse spectral analysis: Estimating frequency-dependent anomalies from nonstationary seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V385-V396
Author(s):  
Jiao Xue ◽  
Chengguo Cai ◽  
Hanming Gu ◽  
Zongjie Li
Keyword(s):  
Spectral Analysis ◽  
Frequency Spectrum ◽  
Seismic Data ◽  
Fourier Spectrum ◽  
Matching Pursuit ◽  
Decomposition Methods ◽  
Seismic Wave Propagation ◽  
Frequency Dependent ◽  
Time Frequency ◽  
Convolution Model

Spectral decomposition has been widely used to detect frequency-dependent anomalies associated with hydrocarbons. By ignoring the time-variant feature of the frequency content of individual reflected wavelets, we have adopted a sparse time-frequency spectrum and developed a matching pursuit-based sparse spectral analysis (MP-SSA) method to estimate the sparse time-frequency representation of the seismic data. Further, we evaluate a generalized nonstationary convolution model concerning propagation attenuation and frequency-dependent reflectivity, and we mathematically evaluate the sparse time-frequency spectrum of the nonstationary seismic data as being equal to the product of the Fourier spectrum of the source wavelet, frequency-dependent reflection coefficient, and the cumulative attenuation during seismic wave propagation. Therefore, the reflectivity spectrum, which is a combination of the frequency-dependent reflectivity and the propagation attenuation, can be determined by dividing the sparse time-frequency spectrum of the seismic data by the Fourier spectrum of the source wavelet. Application of the matching pursuit-based decomposition methods to synthetic nonstationary convolutional data illustrates that the adopted MP-SSA spectrum shows a higher time resolution than the matching pursuit-based Wigner-Ville distribution and the matching pursuit-based instantaneous spectral analysis spectra. Notably, the MP-SSA method can avoid spectral smearing, which may introduce distortions to the frequency-dependent anomaly estimation. Application of the amplitude versus frequency analysis based on MP-SSA to field data illustrates the potential of using the sparse reflectivity spectral intercept and gradient to detect the hydrocarbon reservoirs.

High Resolution Time-Frequency Analysis Base on Ricker Atom Matching Pursuit Decomposition

2013 ◽  
Vol 284-287 ◽  
pp. 3115-3119
Author(s):  
Wei Song ◽  
Jia Hui Zuo ◽  
Peng Cheng Hu
Keyword(s):  
Frequency Analysis ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Seismic Signal ◽  
Dominant Frequency ◽  
Frequency Resolution ◽  
Frequency Variation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Impact

The high accuracy time-frequency representation of non-stationary signals is one of the key researches in seismic signal analysis. Low-frequency part of the seismic data often has a higher frequency resolution, on the contrary it tends to have lower frequency resolution in the high frequency part. It’s difficult to fine characterize the time-frequency variation of non-stationary seismic signals by conventional time-frequency analysis methods due to the limitation of the window function. Therefore based on the Ricker wavelet, we put forward the matching pursuit seismic trace decomposition method. It decomposes the seismic records into a series of single component atoms with different centre time, dominant frequency and energy, by making use of the Wigner-Ville distribution, has the time-frequency resolution of seismic signal reach the limiting resolution of the uncertainty principle and skillfully avoid the impact of interference terms in conventional Wigner-Ville distribution.

Large amplitude ion-acoustic waves observed in the solar wind by the Solar Orbiter

2021 ◽  
Author(s):  
David Pisa ◽  
Jan Soucek ◽  
Ondrej Santolik ◽  
Milan Maksimovic ◽  
Timothy Horbury ◽  
...  
Keyword(s):  
Solar Wind ◽  
Detailed Analysis ◽  
Acoustic Waves ◽  
Large Scale ◽  
Low Frequency ◽  
Linear Wave ◽  
Ion Acoustic Waves ◽  
Ion Acoustic ◽  
The Time Domain ◽  
Low Frequency Waves

<p>Electric field observations of the Time Domain Sampler (TDS) receiver, a part of the Radio and Plasma Waves (RPW) instrument onboard Solar Orbiter, often exhibit very intense broadband emissions at frequencies below 10 kHz in the spacecraft frame. The RPW instrument has been operating almost continuously during the commissioning phase of the mission from March to May, the first perihelion in June, and through the first flyby of Venus in late December 2020. Nearly a year of observations allow us to perform a statistical study of ion-acoustic waves in the solar wind covering an interval of heliocentric distances between 0.5 AU to 1 AU. The occurrence of low-frequency waves peaks around perihelion in June at distances of 0.5 AU and decreases with increasing distances, with only a few waves detected per day in late September at ~1 AU. A more detailed analysis of triggered waveform snapshots shows the typical wave frequency at about 3 kHz and wave power about 5e-2 mV<sup>2</sup>/m<sup>2</sup>. The distribution of the relative phase between two components of the projected E-field in the Spacecraft Reference Frame (SRF) shows a mostly linear wave polarization. These waves are interpreted as strongly Doppler-shifted ion-acoustic waves, generated by solar wind ion beams and often accompany large-scale solar wind structures. A detailed analysis of the Doppler-shift using solar wind data from a Proton and Alpha particle Sensor (PAS), a part of Solar Wind Analyzer (SWA), is done for several examples.</p>

Study on Time-Frequency Spectrum Characteristic of Dynamic Cutting Load Based on Wavelet Regularization

2014 ◽  
Vol 577 ◽  
pp. 196-200 ◽  
Author(s):  
Chun Sheng Liu ◽  
Chun Ping Ren ◽  
Fei Han
Keyword(s):  
Energy Distribution ◽  
Frequency Spectrum ◽  
Low Frequency ◽  
Cutting Process ◽  
Load Spectrum ◽  
Cycle Number ◽  
Conical Pick ◽  
Time Frequency ◽  
Wavelet Regularization ◽  
Spectrum Characteristic

Aimed at investigating time-frequency spectrum characteristic laws of conical pick rotary crushing coal and rock, this paper determines the mapping relationship of load spectrum between energy distribution and cutting process, and achieves quantitative reconstruction of the cutting coal and rock load spectrum. The experimental load spectrum is used as the object of research, utilizing wavelet regularization method to establish load spectrum reconstruction model of the pick cutting coal and rock, which will achieve its reconstruction load spectrum, so that it will discuss the variation of reconstruction load spectrum. Finally, it determines energy distribution of the load spectrum in different frequency band. The results show that the experimental load spectrum energy increases with the increasing of the cutting cycle number, and then decreases, so the characteristics of energy can characterize the cutting process. Energy of reconstruction load spectrum is mainly distributed in the low frequency range, which focuses on 1Hz and 3Hz, the amplitude of the high frequency energy is smaller than others.

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