The Old and the New in Seismic Deconvolution and Wavelet Estimation*

A new method of Vibroseis deconvolution has been recently proposed by the authors. This discussion describes the effects of noise on the application of this method. The initial deconvolution step involves estimating the spectrum of the Vibroseis wavelet by homomorphic filtering. It is shown that noise causes problems with phase estimation. Hence, the Vibroseis wavelet is assumed to be zero phase. Examples demonstrate that zero phase cepstral filtering is a robust wavelet estimation approach for noisy data. The second step of the deconvolution method forms an impulse response model by a spectral extension method. Although this step can improve the resolution of seismic arrivals, it must be applied with caution in view of the deleterious effects of noise.

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“…The problems of phase estimation with noisy data have also been described by de Voogd (1976) and Lines and Ulrych (1977). In homomorphic R.W.…”

Section: Effects Of Noise On Phase Estimationmentioning

confidence: 96%

Impulse Response Models for Noisy Vibroseis† Data*

Lines

Clayton

Ulrych

1980

Geophysical Prospecting

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“…There are three methods to obtain the phase spectrum: (1) Hilbert transform (or Kolmogoroff factorization) method, (2) z transform method, and (3) Wiener-Levinson inverse method. An overview of these methods can be found in WHITE and O'BRIEN (1974), LINES and ULRYCH (1977) and CLAERBOUT (1985).…”

Section: Statistical Gold Deconvolutionmentioning

confidence: 99%

An Approximation to Sparse-Spike Reflectivity Using the Gold Deconvolution Method

Dondurur

2010

Pure Appl. Geophys.

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Wiener deconvolution is generally used to improve resolution of the seismic sections, although it has several important assumptions. I propose a new method named Gold deconvolution to obtain Earth's sparse-spike reflectivity series. The method uses a recursive approach and requires the source waveform to be known, which is termed as Deterministic Gold deconvolution. In the case of the unknown wavelet, it is estimated from seismic data and the process is then termed as Statistical Gold deconvolution. In addition to the minimum phase, Gold deconvolution method also works for zero and mixed phase wavelets even on the noisy seismic data. The proposed method makes no assumption on the phase of the input wavelet, however, it needs the following assumptions to produce satisfactory results: (1) source waveform is known, if not, it should be estimated from seismic data, (2) source wavelet is stationary at least within a specified time gate, (3) input seismic data is zero offset and does not contain multiples, and (4) Earth consists of sparse spike reflectivity series. When applied in small time and space windows, the Gold deconvolution algorithm overcomes nonstationarity of the input wavelet. The algorithm uses several thousands of iterations, and generally a higher number of iterations produces better results. Since the wavelet is extracted from the seismogram itself for the Statistical Gold deconvolution case, the Gold deconvolution algorithm should be applied via constant-length windows both in time and space directions to overcome the nonstationarity of the wavelet in the input seismograms. The method can be extended into a two-dimensional case to obtain time-and-space dependent reflectivity, although I use onedimensional Gold deconvolution in a trace-by-trace basis. The method is effective in areas where small-scale bright spots exist and it can also be used to locate thin reservoirs. Since the method produces better results for the Deterministic Gold deconvolution case, it can be used for the deterministic deconvolution of the data sets with known source waveforms such as land Vibroseis records and marine CHIRP systems.

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“…Derivation of the above equation can be done using the Hilbert transform of the logarithm of the wavelet's amplitude spectrum as stated in [23] thus the method's name.…”

Section: ) the Hilbert Transformor Kolmogorov Factorization Methodsmentioning

confidence: 99%

Evaluation of the Sensitivity of Seismic Inversion Algorithms to Different Statistically Estimated Wavelets

Gomes¹,

Santos²,

Burgos³

et al. 2017

IJERA

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Seismic wavelet estimation is an important step in processing and analysis of seismic data. Inversion methods as Narrow-Band and theConstrained Sparse-Spike ones require information about it so that the inversion solution, once it is not a unique problem, may be restricted by comparing the real seismic trace with the synthetic generated by convolution of the estimated reflectivity and wavelet. Besides helping in seismic inversion, a good estimate of the wavelet enables an inverse filter with less uncertainty to be computed in the deconvolution step and while tying well logs, a better correlation between the seismic trace and well log can be achieved. Depending on the use or not of well log information, the methods of wavelet estimation can be divided into two classes: statistical and deterministic. This work aimed to test the sensitivity of acoustic post-stack seismic inversion algorithms to wavelets statistically estimated by two distinct methods.

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The Old and the New in Seismic Deconvolution and Wavelet Estimation*

Cited by 50 publications

References 30 publications

Impulse Response Models for Noisy Vibroseis† Data*

Impulse Response Models for Noisy Vibroseis† Data*

An Approximation to Sparse-Spike Reflectivity Using the Gold Deconvolution Method

Evaluation of the Sensitivity of Seismic Inversion Algorithms to Different Statistically Estimated Wavelets

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