Comparisons of adaptive subtraction methods for multiple attenuation

Abma, Ray; Kabir, Nurul; Matson, Ken H.; Michell, Scott; Shaw, Sylvie; McLain, Bill

doi:10.1190/1.1895312

Cited by 58 publications

(21 citation statements)

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“…Moreover, 2D SRME produces errors in the predicted multiples because of 3D complexity of the earth ͑Dragoset and Jeričević, 1998; Ross et al, 1999;Verschuur, 2006͒, whereas recently developed full 3D-SRME algorithms can suffer from imperfections related to incomplete acquisitions ͑see, e.g., Lin et al, 2004;Moore and Dragoset, 2004;van Borselen et al, 2004;van Dedem and Verschuur, 2005͒, including erroneous reconstructions of missing near offsets ͑Dragoset and Jeričević, 1998͒. For field data, these factors preclude iterative SRME, resulting in amplitude errors that vary for different multiple orders ͑see, e.g., Verschuur and Berkhout, 1997;Paffenholz et al, 2002͒. In practice, the second separation stage appears to be particularly challenging because adaptive ᐉ 2 -matched-filtering techniques are known to lead to residual multiple energy, high-frequency clutter, and deterioration of the primaries ͑Chen et al, 2004;Abma et al, 2005;Herrmann et al, 2007a͒. By employing the ability of the curvelet transform ͑Candes et al., 2006;Hennenfent and Herrmann, 2006͒ to detect wavefronts with conflicting dips ͑e.g., caustics͒, Herrmann et al ͑2007a͒ and Herrmann et al ͑2008b͒ derived a nonadaptive separation scheme ͑independent of the total data͒ that uses the original data and SRME-predicted multiples as input and produces an estimate for the primaries.…”

Section: Introductionmentioning

confidence: 99%

Adaptive curvelet-domain primary-multiple separation

2008

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In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination ͑SRME͒, amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways ͑as a function of offset, time, and dip͒, they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale ͑fre-quency band͒, and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.

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Section: Introductionmentioning

confidence: 99%

Adaptive curvelet-domain primary-multiple separation

2008

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“…Adaptive subtraction involves a matching filter to compensate for the amplitude, phase, and frequency distortions in the predicted noise model. Techniques for matching filtering and adaptive subtraction have been developed and discussed by a number of authors ͑Verschuur et al, 1992; Monk, 1993;Spitz, 1999;van Borselen et al, 2003;Wang, 2003;Guitton and Verschuur, 2004;Abma et al, 2005;Lu and Mao, 2005;Denisov et al, 2006͒. The regularized nonstationary regression technique proposed in this paper allows the matching filter to become smoothly nonstationary without the need to break the input data into local windows.…”

Section: Introductionmentioning

confidence: 94%

Adaptive multiple subtraction using regularized nonstationary regression

Fomel

2009

GEOPHYSICS

133

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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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“…Unfortunately, none of these prediction-based methods can provide a perfect prediction of the multiples because of phase, wavelet, or space-shift errors (Abma et al, 2005). Therefore, a second step, usually referred to as adaptive multiple subtraction, is required to accommodate the prediction to the actual multiples before the subtraction.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multiple subtraction: Unification and comparison of matching filters based on the ℓq-norm and statistical independence

et al. 2016

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An adaptive multiple subtraction step is necessary for almost all methods that predict seismic multiple reflected waves. We aim at giving a better understanding of matching filters based on l q -norms and on statistical independence. We found that the formulation of all of these techniques can be gathered in a mutual framework by introducing a space-time operator, called the primary enhancer, acting on the estimated primaries. The differences between the considered matching filters become more intuitive because this operator behaves as a simple amplitude compressor. In this perspective, all the methods tend to uncorrelate the predicted multiples and the enhanced estimated primaries. The study of these matching-filter methods can be narrowed to the study of the primary enhancer operator because it is the only difference. Moreover, we have emphasized the role of using adjacent traces or windowing approaches in terms of statistics, and we show that an adequate windowing strategy may overbear the choice of the objective function. Indeed, our analysis showed that setting a good windowing strategy may be more important than changing the classical leastsquares adaptation criterion to other approaches based on l q -norm minimization or independent component analysis.

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Comparisons of adaptive subtraction methods for multiple attenuation

Cited by 58 publications

References 0 publications

Adaptive curvelet-domain primary-multiple separation

Adaptive curvelet-domain primary-multiple separation

Adaptive multiple subtraction using regularized nonstationary regression

Adaptive multiple subtraction: Unification and comparison of matching filters based on the ℓq-norm and statistical independence

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