Lateral prediction for noise attenuation by <i>t-x</i> and <i>f-x</i> techniques

Abma, Ray; Claerbout, Jon F.

doi:10.1190/1.1443920

Cited by 297 publications

(68 citation statements)

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“…If a f-x rather than the f-xy interpolator was applied within a shot gather, representation of curved events would have difficult. The f-x-y domain prediction, however, can relax the requirement that events be linear (Chase, 1992;Abma and Claerbout, 1995;Gülünay, 2000). Events that are nonlinear in one direction but linear in another may be predicted exactly with the f-x-y prediction filter.…”

Section: Discussion and Examplesmentioning

confidence: 99%

Seismic trace interpolation in the f‐x‐y domain

Wang¹

2002

GEOPHYSICS

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Seismic trace interpolation is implemented as a 2-D (x, y) spatial prediction, performed separately on each frequency ( f ) slice. This so-called f-x-y domain trace interpolation method is based on the relation that the linear prediction (LP) operator estimated at a given frequency may be used to predict data at a higher frequency but a smaller trace spacing. The relationship originally given for the f-x domain trace interpolation is successfully extended to the f-x-y domain. The extension is achieved by masking the data samples selectively from the input frequency slice to design the LP operators. Two interpolation algorithms using the full-step and the fractional-step predictions, respectively, are developed. Both methods use an all-azimuth prediction in the x-y domain, but the fractional-step prediction method is computationally more efficient. While the interpolation method can be applied to a common-offset cube of 3-D seismic, it can also be applied to 2-D seismic traces for prestack data processing. Synthetic and real data examples demonstrate the capability of the interpolation method.

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Section: Discussion and Examplesmentioning

confidence: 99%

Seismic trace interpolation in the f‐x‐y domain

Wang¹

2002

GEOPHYSICS

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“…As an alternative to semblance-weighted slant stacks, spatial prediction filtering (Hornbostel, 1991;Abma and Claerbout, 1995) in either t-x or f -x domain can be considered for signal reconstruction, since these techniques are also able to give an estimate for "missing" energy. However, it remains explicitly assumed that events are at least locally linear; furthermore, it is unclear if these techniques are able to deal with signals displaying phase perturbations.…”

Section: Discussionmentioning

confidence: 99%

“…Among these are the skeletonization method (Le and Nyland, 1990;Lu and Cheng, 1990), the statistical method of Hansen et al (1988), methods using local τ -p analyses (Harlan et al, 1984;Kong et al, 1985), and spatial-prediction filtering (Hornbostel, 1991;Abma and Claerbout, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…This approach replaces samples by their predicted values based on information contained in adjacent traces. The laterally coherent signal energy can be extracted using two different techniques; least-mean-square adaptive filtering in the t-x domain (Hornbostel, 1991) or a Wiener filter in either the f -x or t-x domains (Hornbostel, 1991;Abma and Claerbout, 1995).…”

Section: Introductionmentioning

confidence: 99%

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Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Baan

Ambert

2000

GEOPHYSICS

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Article: Van der Baan, M. and Paul, A. (2000) Recognition and reconstruction of coherent energy with application to deep seismic reflection data. Geophysics, 65 (2 Recognition and reconstruction of coherent energy with application to deep seismic reflection dataMirko van der Baan * and Anne Paul ‡ ABSTRACTReflections in deep seismic reflection data tend to be visible on only a limited number of traces in a common midpoint gather. To prevent stack degeneration, any noncoherent reflection energy has to be removed.In this paper, a standard classification technique in remote sensing is presented to enhance data quality. It consists of a recognition technique to detect and extract coherent energy in both common shot gathers and final stacks. This technique uses the statistics of a picked seismic phase to obtain the likelihood distribution of its presence. Multiplication of this likelihood distribution with the original data results in a "cleaned up" section. Application of the technique to data from a deep seismic reflection experiment enhanced the visibility of all reflectors considerably.Because the recognition technique cannot produce an estimate of "missing" data, it is extended with a reconstruction method. Two methods are proposed: application of semblance weighted local slant stacks after recognition, and direct recognition in the linear τ -p domain. In both cases, the power of the stacking process to increase the signal-to-noise ratio is combined with the direct selection of only specific seismic phases. The joint application of recognition and reconstruction resulted in data images which showed reflectors more clearly than application of a single technique.

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“…A random noise attentipn has been. successfully addressed by Canales 1984), Gulunay (1986), Abma and Claerbout (1995), Soubaras (1995), and others. A more challenging problem of coherent noise attenuation has only recently joined the circle of the prediction…”

Section: Signal and Noise Separationmentioning

confidence: 99%

Applications of plane‐wave destruction filters

2002

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Plane‐wave destruction filters originate from a local plane‐wave model for characterizing seismic data. These filters can be thought of as a time–distance (T‐X) analog of frequency‐distance (F‐X) prediction‐error filters and as an alternative to T‐X prediction‐error filters. The filters are constructed with the help of an implicit finite‐difference scheme for the local plane‐wave equation. Several synthetic and real data examples show that finite‐difference plane‐wave destruction filters perform well in applications such as fault detection, data interpolation, and noise attenuation.

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Lateral prediction for noise attenuation by t-x and f-x techniques

Cited by 297 publications

References 0 publications

Seismic trace interpolation in the f‐x‐y domain

Seismic trace interpolation in the f‐x‐y domain

Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Applications of plane‐wave destruction filters

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