Seismic time-frequency spectral decomposition by matching pursuit
Author(s) -
Yanghua Wang
Publication year - 2007
Publication title -
geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 172
eISSN - 1942-2156
pISSN - 0016-8033
DOI - 10.1190/1.2387109
Subject(s) - wavelet , matching pursuit , seismic trace , morlet wavelet , computer science , time–frequency analysis , instantaneous phase , wavelet packet decomposition , acoustics , algorithm , attenuation , gabor wavelet , pattern recognition (psychology) , wavelet transform , filter (signal processing) , physics , artificial intelligence , optics , discrete wavelet transform , computer vision , compressed sensing
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 spect...
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