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Adaptive multiple subtraction using regularized nonstationary regression

Sergey Fomel
Sergey Fomel
1The University of Texas at Austin, Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, Austin, Texas, U.S.A. E-mail:
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January 01, 2010


Stationary regression is the backbone of seismic data-processing algorithms including match filtering, which is commonly applied for adaptive multiple subtraction. However, the assumption of stationarity is not always adequate for describing seismic signals. I have developed a general method of nonstationary regression and that applies to nonstationary match filtering. The key idea is the use of shaping regularization to constrain the variability of nonstationary regression coefficients. Simple computational experiments demonstrate advantages of shaping regularization over classic Tikhonov’s regularization, including a more intuitive selection of parameters and a faster iterative convergence. Using benchmark synthetic data examples, I have successfully applied this method to the problem of adaptive subtraction of multiple reflections.

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Geophysical References Series

Geophysics Today: A Survey of the Field as the Journal Celebrates its 75th Anniversary

Sergey Fomel
Sergey Fomel
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Society of Exploration Geophysicists
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Publication date:
January 01, 2010




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