Pattern recognition, spatial predictability, and subtraction of multiple events

Spitz, Simon

doi:10.1190/1.1438154

Cited by 83 publications

(28 citation statements)

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“…The signal being unknown, we can approximate the signal by deconvolving the data PEFs D estimated from the data by the noise PEFs N estimated from the multiple model (Spitz, 1999). Therefore, for the adaptive subtraction of multiples, we can improve our estimation of the signal by introducing different norms like the 1 norm when the multiples are weak or by adding the signal covariance operator when the signal has not minimum energy.…”

Section: Adaptive Subtraction Of Predicted Multiplesmentioning

confidence: 99%

What can we do with a model of the multiples?

Guitton¹

2003

65th EAGE Conference &Amp; Exhibition

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Section: Adaptive Subtraction Of Predicted Multiplesmentioning

confidence: 99%

What can we do with a model of the multiples?

Guitton¹

2003

65th EAGE Conference &Amp; Exhibition

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“…Recent approaches for multiple suppression (Spitz, 1999;Bednar and Neale, 1999) operate in the f − x domain, balancing a definite speed advantage over t − x domain techniques with the limiting assumption that the data be time-stationary. Since ground roll is often highly dispersive, and thus temporally nonstationary, a t − x domain approach is a more appropriate choice for ground roll removal.…”

Section: Introductionmentioning

confidence: 99%

(t,x) domain, pattern‐based ground roll removal

Brown¹,

Clapp²

2000

SEG Technical Program Expanded Abstracts 2000

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We demonstrate the use of a new, t − x domain, pattern-based signal/noise separation technique to separate ground roll from primary reflection events. Ground roll is notoriously difficult to model with generality, but the technique requires a kinematically correct model of the noise. We obtain an imperfect model of the ground roll directly from the data itself, by application of a suitable lowpass filter. On a 2-D receiver line gather taken from a 3-D shot gather, in which the ground roll is spatially aliased and has nonlinear moveout, the separation results improve markedly over direct subtraction of the noise model from the data.

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“…The existing multiple suppression techniques can be classified into two major categories (Weglein, 1999): (1) the filtering methods based on the differences between primaries and multiples (Lu et al, 2003) and (2) the adaptive multiple subtraction methods based on multiple prediction (Verschuur et al, 1992;Spitz, 1999;Lu, 2006).…”

Section: Introductionmentioning

confidence: 99%

An improved predictive deconvolution based on maximization of non-Gaussianity

2008

Appl. Geophys.

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The predictive deconvolution algorithm (PD), which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data (primaries and multiples) have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm (IPD) by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.

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Pattern recognition, spatial predictability, and subtraction of multiple events

Cited by 83 publications

References 0 publications

What can we do with a model of the multiples?

What can we do with a model of the multiples?

(t,x) domain, pattern‐based ground roll removal

An improved predictive deconvolution based on maximization of non-Gaussianity

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