Processing via spectral modeling

Rosa, Andre Luiz Romanelli; Ulrych, Tadeusz J.

doi:10.1190/1.1443144

Cited by 87 publications

(25 citation statements)

References 4 publications

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“…We obtained interesting results while computing C-and L-moment derived pdf 's for some realizations of re¯ection coef®cients obtained from well logs in the Campos basin, offshore Brazil (Rosa and Ulrych, 1991). We examined many different logs but show the results of only one for obvious reasons.…”

Section: Field Examplesmentioning

confidence: 99%

L-moments and C-moments

Ulrych

Velis

Woodbury

et al. 2000

Stochastic Environmental Research and Risk Assessment (SERRA)

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It is well known that the computation of higher order statistics, like skewness and kurtosis, (which we call C-moments) is very dependent on sample size and is highly susceptible to the presence of outliers. To obviate these dif®culties, Hosking (1990) has introduced related statistics called L-moments. We have investigated the relationship of these two measures in a number of different ways. Firstly, we show that probability density functions (pdf ) that are estimated from L-moments are superior estimates to those obtained using C-moments and the principle of maximum entropy. C-moments computed from these pdf 's are not however, contrary to what one may have expected, better estimates than those estimated from sample statistics. L-moment derived distributions for ®eld data examples appear to be more consistent sample to sample than pdf 's determined by conventional means. Our observations and conclusions have a signi®cant impact on the use of the conventional maximum entropy procedure which typically uses C-moments from actual data sets to infer probabilities.

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Section: Field Examplesmentioning

confidence: 99%

L-moments and C-moments

Ulrych

Velis

Woodbury

et al. 2000

Stochastic Environmental Research and Risk Assessment (SERRA)

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“…At constant time t, the time-dependent wavelet only depends on frequency; thus, spectral simulation methods can be used to fi t the wavelet. Based on the conclusions of Ricker (1977), Rosa et al (1991) pointed out that as long as the seismic wavelet spectrum is smooth, it can be fitted. However, the abovementioned smooth amplitude spectrum is not what we typically refer to as locally smooth function.…”

Section: Amplitude Spectrum Compensationmentioning

confidence: 99%

“…Thus, we improve the seismic resolution by eliminating the effect of the seismic wavelet and processing in the time-frequency domain with the generalized S transform. Seismic modeling in the time-frequency domain can be treated as time-dependent with the time-dependent wavelet as a pseudodifferential operator (Rosa, 1991), a rather difficult problem to solve. At constant time t, the time-dependent wavelet only depends on frequency; thus, spectral simulation methods can be used to fi t the wavelet.…”

Section: Amplitude Spectrum Compensationmentioning

confidence: 99%

Amplitude spectrum compensation and phase spectrum correction of seismic data based on the generalized S transform

Zhou

Wang

et al. 2014

Appl. Geophys.

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We propose a method for the compensation and phase correction of the amplitude spectrum based on the generalized S transform. The compensation of the amplitude spectrum within a reliable frequency range of the seismic record is performed in the S domain to restore the amplitude spectrum of refl ection. We use spectral simulation methods to fi t the time-dependent amplitude spectrum and compensate for the amplitude attenuation owing to absorption. We use phase scanning to select the time-, space-, and frequencydependent phases correction based on the parsimony criterion and eliminate the residual phase effect of the wavelet in the S domain. The method does not directly calculate the Q value; thus, it can be applied to the case of variable Q. The comparison of the theory model and fi eld data verify that the proposed method can recover the amplitude spectrum of the strata refl ectivity, while eliminating the effect of the residual phase of the wavelet. Thus, the wavelet approaches the zero-phase wavelet and, the seismic resolution is improved.

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“…Where k is a constant, N is the order, a n is the polynomial coefficient about f, and 0 3, 2 7 kN     generally [13].…”

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confidence: 99%

Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

Zhang¹,

Dai²,

Zhang³

et al. 2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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Abstract-To adjust to the development of dynamic prediction of oil and gas reservoir, exploration of subtle reservoir, exploration and development of thin interbeds, processed seismic profiles are required to be of high signal to noise ratio, high resolution and high fidelity. While the time-varying wavelet in seismic data is an important factor affecting the quality of seismic profiles, for the purpose of improving the effects of seismic data processing, the influence law of wavelet time-varying nature is necessarily to be analyzed and researched, so that the timevarying wavelet can be suppressed based on more targeted methods. In the paper, the formation mechanism of time-varying nature is studied, and the influence law of time-varying wavelet is analyzed in detail, which is different in various links of seismic data processing, including amplitude compensation, pre-stack deconvolution, post-stack deconvolution and migration. Meanwhile the methods and application effects for suppressing wavelet time-varying nature in every link are summarized, and the advantages and disadvantages of these approaches are concluded through analysis and comparison, so that the development direction of researching more effective methods for suppressing or eliminating wavelet time-varying nature in the future is pointed out.

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Processing via spectral modeling

Cited by 87 publications

References 4 publications

L-moments and C-moments

L-moments and C-moments

Amplitude spectrum compensation and phase spectrum correction of seismic data based on the generalized S transform

Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

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