An Amplitude Preserving S-Transform for Seismic Data Attenuation Compensation

The S-transform (ST), as a time-frequency analysis tool, has been widely used, but the amplitude preserving property is a little poor near the boundary of the selected discrete signal. The reason lies that the summation of the product between the analytical window and the comprehensive window over the sliding step deviates from unity near the boundary in the discrete cases. In order to hold the amplitude preserving property for the discrete signal recovery analysis, an amplitude preserving S-transform (APST) is proposed based on a novel analytical window selection. First, lots of numerical tests are used to analyze the shortcomings of the ST near the boundary for the selected discrete signal and demonstrate the effectiveness and the validity of the proposed APST using the novel analytical window. After that, the proposed APST is used for seismic data attenuation compensation, during which the attenuation function is estimated based on the minimum phase assumption using a statistical variable-step hyperbolic smoothing method. Numerical examples on synthetic and field data demonstrate the validity of the proposed method using the seismogram and time-frequency spectrum comparisons. Besides, the proposed APST can be easily extended into a generalized ST which is more flexible compared with the ST, and it can also be used in seismology, remote sensing, and other related discrete signal analysis fields.

[1]  Li Jingye,et al.  A Stable and Efficient Attenuation Compensation Method based on Inversion , 2015 .

[2]  Estimation of Q factors from reflection seismic data for a band-limited and stabilized inverse Q filter driven by an average-Q model , 2014 .

[3]  Yanghua Wang,et al.  A stable and efficient approach of inverse Q filtering , 2002 .

[4]  Xuekai Sun,et al.  Q estimation using modified S transform based on pre-stack gathers and its applications on carbonate reservoir , 2015 .

[5]  Benfeng Wang,et al.  Inversion based data-driven attenuation compensation method , 2014 .

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

[7]  Xiaohong Chen,et al.  Absorption-compensation method by l1-norm regularization , 2014 .

[8]  Mirko van der Baan,et al.  The robustness of seismic attenuation measurements using fixed- and variable-window time-frequency transforms , 2009 .

[9]  Gary F. Margrave,et al.  Theory of nonstationary linear filtering in the Fourier domain with application to time‐variant filtering , 1998 .

[10]  Yanghua Wang,et al.  Q analysis on reflection seismic data , 2004 .

[11]  Wei Huang,et al.  Absorption decomposition and compensation via a two-step scheme , 2015 .

[12]  Chunhua Hu,et al.  Seismic Attenuation Estimation Using an Improved Frequency Shift Method , 2013, IEEE Geoscience and Remote Sensing Letters.

[13]  Jinghuai Gao,et al.  Seismic Quality Factors Estimation From Spectral Correlation , 2008, IEEE Geoscience and Remote Sensing Letters.

[14]  R. P. Lowe,et al.  Pattern analysis with two-dimensional spectral localisation: Applications of two-dimensional S transforms , 1997 .

[15]  Sanyi Yuan,et al.  Sparse reflectivity inversion for nonstationary seismic data , 2014 .

[16]  Yanghua Wang Stable Q Analysis on Vertical Seismic Profiling Data , 2014 .

[17]  C. Robert Pinnegar,et al.  The S-transform with windows of arbitrary and varying shape , 2003 .

[18]  Guochang Liu,et al.  A novel nonstationary deconvolution method based on spectral modeling and variable-step sampling hyperbolic smoothing , 2014 .

[19]  David C. Henley,et al.  Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data , 2011 .