Application of bi-Gaussian S-transform in high-resolution seismic time-frequency analysis

AbstractThe S-transform is one of the most widely used methods of time-frequency analysis. It combines the respective advantages of the short-time Fourier transform and wavelet transforms with scale-dependent resolution using Gaussian windows, scaled inversely with frequency. One of the problems with the traditional symmetric Gaussian window is the degradation of time resolution in the time-frequency spectrum due to the long front taper. We have studied the performance of an improved S-transform with an asymmetric bi-Gaussian window. The asymmetric bi-Gaussian window can obtain an increased time resolution in the front direction. The increased time resolution can make event picking high resolution, which will facilitate an improved time-frequency characterization for oil and gas trap prediction. We have applied the slightly modified bi-Gaussian S-transform to a synthetic trace, a 2D seismic section, and a 3D seismic cube to indicate the superior performance of the bi-Gaussian S-transform in analyzing nons...

[1]  Sergey Fomel,et al.  Seismic data decomposition into spectral components using regularized nonstationary autoregression , 2012 .

[2]  Wei Liu,et al.  Seismic Time–Frequency Analysis via Empirical Wavelet Transform , 2016, IEEE Geoscience and Remote Sensing Letters.

[3]  W. Liu,et al.  Seismic Time-frequency Analysis Using Improved Complete Ensemble Empirical Mode Decomposition , 2016 .

[4]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[5]  Kurt J. Marfurt,et al.  Instantaneous spectral attributes to detect channels , 2007 .

[6]  Yangkang Chen,et al.  Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Wavelet Transform , 2014 .

[7]  C. Robert Pinnegar,et al.  The Bi-Gaussian S-Transform , 2002, SIAM J. Sci. Comput..

[8]  Charles Robert Pinnegar The generalized S-transform and TT-transform, in one and two dimensions , 2002 .

[9]  Tianyou Liu,et al.  Seismic spectral decomposition and analysis based on Wigner–Ville distribution for sandstone reservoir characterization in West Sichuan depression , 2010 .

[10]  Yangkang Chen,et al.  Random noise attenuation using local signal-and-noise orthogonalization , 2015 .

[11]  Jing-Hua Gao,et al.  Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Transform , 2014, IEEE Geoscience and Remote Sensing Letters.

[12]  D. Okaya,et al.  Frequency‐time decomposition of seismic data using wavelet‐based methods , 1995 .

[13]  Mirko van der Baan,et al.  Empirical mode decomposition for seismic time-frequency analysis , 2013 .

[14]  Bo Zhang,et al.  Spectral decomposition of time- vs. depth-migrated data , 2013 .

[15]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[16]  Jont B. Allen,et al.  Short term spectral analysis, synthesis, and modification by discrete Fourier transform , 1977 .

[17]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[18]  Patrick Flandrin,et al.  On the existence of discrete Wigner distributions , 1999, IEEE Signal Processing Letters.

[19]  G. Zhang,et al.  Seismic Data Analysis Using Synchrosqueezeing Wavelet Transform - A Case Study Applied to Boonsville Field , 2015 .

[20]  Mirko van der Baan,et al.  Applications of the synchrosqueezing transform in seismic time-frequency analysis , 2014 .