Inversion-Driven Attenuation Compensation Using Synchrosqueezing Transform

Attenuation is a fundamental mechanism as seismic wave propagates through the earth. The loss of high-frequency energy and concomitant phase distortion can be compensated by inverse <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> filtering to enhance the resolution of seismic data. Since the attenuation process depends on time and frequency, it is routinely performed in the time–frequency domain. The synchrosqueezing transform (SST), which provides highly localized time–frequency representations for the nonstationary signals due to reduced spectral smearing, is applied to implement the inverse <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> filtering scheme. However, the amplitude compensation process is unstable because energy amplification is involved. To stabilize it, the amplitude compensation is regarded as an inverse problem with an L1-norm regularization term in the SST domain. The iteratively reweighted least-squares algorithm is used to solve the regularized inverse problem. Synthetic and real data examples illustrate the stability and effectiveness of the proposed method.

[1]  H. Kolsky,et al.  LXXI. The propagation of stress pulses in viscoelastic solids , 1956 .

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

[3]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

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

[5]  Sylvain Meignen,et al.  Second-Order Synchrosqueezing Transform or Invertible Reassignment? Towards Ideal Time-Frequency Representations , 2015, IEEE Transactions on Signal Processing.

[6]  Mirko van der Baan,et al.  Spectral estimation—What is new? What is next? , 2014 .

[7]  Mirko van der Baan,et al.  Attenuation estimation using high resolution time-frequency transforms , 2017, Digit. Signal Process..

[8]  Benfeng Wang An Amplitude Preserving S-Transform for Seismic Data Attenuation Compensation , 2016, IEEE Signal Processing Letters.

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

[10]  Hau-Tieng Wu,et al.  The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications , 2011, Signal Process..

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

[12]  Wei Zhao,et al.  Enhancing resolution of nonstationary seismic data by molecular-Gabor transform , 2013 .

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

[14]  Hau-Tieng Wu,et al.  Synchrosqueezing-Based Recovery of Instantaneous Frequency from Nonuniform Samples , 2010, SIAM J. Math. Anal..

[15]  D. Oldenburg,et al.  NON-LINEAR INVERSION USING GENERAL MEASURES OF DATA MISFIT AND MODEL STRUCTURE , 1998 .

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

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

[18]  Alireza Ahmadifard,et al.  Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition , 2016 .

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

[20]  Jianzhong Zhang,et al.  Synchrosqueezing S-Transform and Its Application in Seismic Spectral Decomposition , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[22]  Mirko van der Baan,et al.  Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution? , 2012 .

[23]  Sylvain Meignen,et al.  Time-Frequency Reassignment and Synchrosqueezing: An Overview , 2013, IEEE Signal Processing Magazine.

[24]  Mokhtar Mohammadi,et al.  Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation , 2017, IEEE Transactions on Geoscience and Remote Sensing.

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