A fast automatic multichannel blind seismic inversion for high-resolution impedance recovery

ABSTRACTThe inversion of seismic reflection data for acoustic impedance (AI) is a common and accepted method for the interpretation of poststack seismic data. The original mathematical problem is nonlinear due to the nonlinearity of relation between the AI and the earth reflectivity series and also the insufficiency of information about the source wavelet. Furthermore, the problem is ill posed due to the lack of low and high frequencies in the data. We have developed a multichannel blind inversion and solved it for obtaining the AI model and the wavelet directly from seismic reflection data. We found a solution to the overall problem by alternating between two subproblems, corresponding to the AI and wavelet recovery. Having an estimation of the wavelet, the algorithm directly inverts multichannel data for a high-resolution AI model, having blocky structures in the sense of total variation (TV) regularization, while satisfying a priori low-frequency information. The advantages of the split Bregman techniq...

[1]  Ali Gholami,et al.  A balanced combination of Tikhonov and total variation regularizations for reconstruction of piecewise-smooth signals , 2013, Signal Process..

[2]  Mirko van der Baan,et al.  Robust wavelet estimation and blind deconvolution of noisy surface seismics , 2007 .

[3]  Mauricio D. Sacchi,et al.  Fast 3D Blind Seismic Deconvolution via Constrained Total Variation and GCV , 2013, SIAM J. Imaging Sci..

[4]  F. Moraes,et al.  Nonlinear impedance inversion for attenuating media , 2009 .

[5]  W. Schneider,et al.  Generalized linear inversion of reflection seismic data , 1983 .

[6]  Mauricio D. Sacchi,et al.  Sparse multichannel blind deconvolution , 2014 .

[7]  Wotao Yin,et al.  Error Forgetting of Bregman Iteration , 2013, J. Sci. Comput..

[8]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[9]  D. Oldenburg,et al.  Recovery of the acoustic impedance from reflection seismograms , 1983 .

[10]  T. Ulrych,et al.  Autoregressive recovery of the acoustic impedance , 1983 .

[11]  Yanfei Wang,et al.  Seismic impedance inversion using l 1-norm regularization and gradient descent methods , 2010 .

[12]  D. Velis Stochastic sparse-spike deconvolution , 2008 .

[13]  Bjørn Ursin,et al.  Approximate computation of the acoustic impedance from seismic data , 1983 .

[14]  Rui Zhang,et al.  Seismic sparse-layer reflectivity inversion using basis pursuit decomposition , 2011 .

[15]  Igor B. Morozov,et al.  Accurate poststack acoustic-impedance inversion by well-log calibration , 2009 .

[16]  Ali Gholami,et al.  Nonlinear multichannel impedance inversion by total-variation regularization , 2015 .

[17]  R. O. Lindseth Synthetic sonic logs; a process for stratigraphic interpretation , 1979 .

[18]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[19]  Kjetil F. Kaaresen,et al.  Multichannel blind deconvolution of seismic signals , 1998 .

[20]  Xin-Quan Ma,et al.  A constrained global inversion method using an overparameterized scheme : Application to poststack seismic data , 2001 .

[21]  Sanyi Yuan,et al.  Simultaneous multitrace impedance inversion with transform-domain sparsity promotionStructures — Exploring impedance inversion , 2015 .

[22]  Mauricio D. Sacchi,et al.  A Fast and Automatic Sparse Deconvolution in the Presence of Outliers , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[24]  D. Bartel,et al.  Uncertainties in low-frequency acoustic impedance models , 2007 .

[25]  A. Gholami,et al.  Regularization of linear and non-linear geophysical ill-posed problems with joint sparsity constraints , 2010 .

[26]  Adam Pidlisecky,et al.  Multitrace impedance inversion with lateral constraints , 2015 .