A combined time-frequency filtering strategy for Q-factor compensation of poststack seismic data

ABSTRACTAttenuation is one of the main factors responsible for limiting resolution of the seismic method. It selectively damps the higher frequency components of the signal more strongly, causing the earth to work as a low-pass filter. This loss of high-frequency energy may be partially compensated by application of inverse Q filtering routines. However, such routines often increase the noise level of the data, thereby restricting their use. These filters also require a quality factor profile as an input parameter, which is rarely available. In recent years, alternative methods for stable inverse Q filtering have been presented in the literature, which makes it possible to correct the attenuation without introducing so much noise. In addition, new methods have been proposed to estimate the quality factor from seismic reflection data. We have developed a three-stage workflow oriented for attenuation correction in stacked sections. In the first stage, a trace-by-trace estimate of the quality factor is perfo...

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

[2]  S. A. M. Oliveira,et al.  Q factor estimation from the amplitude spectrum of the time–frequency transform of stacked reflection seismic data , 2015 .

[3]  R. Tonn,et al.  THE DETERMINATION OF THE SEISMIC QUALITY FACTOR Q FROM VSP DATA: A COMPARISON OF DIFFERENT COMPUTATIONAL METHODS1 , 1991 .

[4]  Yanghua Wang,et al.  Inverse Q-filter for seismic resolution enhancement , 2006 .

[5]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[6]  Roger A. Clark,et al.  Estimation of Q from surface seismic reflection data , 1998 .

[7]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook Introductory Theory And Applications In Science , 2002 .

[8]  Tadeusz J. Ulrych,et al.  Seismic absorption compensation: A least squares inverse scheme , 2007 .

[9]  Michal Malinowski,et al.  Application of curvelet denoising to 2D and 3D seismic data — Practical considerations , 2014 .

[10]  Wagner Moreira Lupinacci,et al.  L1 norm inversion method for deconvolution in attenuating media , 2013 .

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

[12]  Clifford H. Thurber,et al.  Parameter estimation and inverse problems , 2005 .

[13]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook , 2002 .

[14]  Yanghua Wang,et al.  Seismic Inverse Q Filtering , 2008 .

[15]  Einar Kjartansson,et al.  Constant Q-wave propagation and attenuation , 1979 .

[16]  Carl Taswell,et al.  The what, how, and why of wavelet shrinkage denoising , 2000, Comput. Sci. Eng..

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

[18]  S. M. Doherty,et al.  Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data , 2000 .

[19]  Yanghua Wang Stable Q analysis on vertical seismic profiling data , 2014 .

[20]  Walter E. Medeiros,et al.  Estimating quality factor from surface seismic data: A comparison of current approaches , 2011 .

[21]  Time–frequency spectral signature of Pelotas Basin deep water gas hydrates system , 2010 .

[22]  Fernando S. Moraes,et al.  High-resolution gathers by inverse Q filtering in the wavelet domain , 2013 .