On: “Predictive deconvolution and the zero‐phase source” by B. Gibson and K. Larner (GEOPHYSICS, 49, 379–397, April 1984).

Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 172-172
Author(s):  
W. Harry Mayne
Keyword(s):  
Frequency Domain ◽  
Phase Correction ◽  
Time Function ◽  
Predictive Deconvolution ◽  
Intractable Problem ◽  
Zero Phase

The authors are to be complimented on a most courageous attempt to solve a difficult (and perhaps intractable) problem. As an active advocate of using nonlinear sweeps to combat earth attenuation, I was very interested in the results reported when inverse Q-filters were applied. Use of an appropriately selected logarithmic time function sweep (dB/Hz response in the frequency domain) can provide the necessary amplitude (but not the phase) correction of any chosen inverse Q-filter.

Predictive deconvolution and the zero‐phase source

Geophysics ◽  
1984 ◽  
Vol 49 (4) ◽  
pp. 379-397 ◽  
Author(s):  
Bruce Gibson ◽  
Ken Larner
Keyword(s):  
White Noise ◽  
Field Data ◽  
Synthetic Data ◽  
Phase Correction ◽  
Phase Spectrum ◽  
Signal Spectrum ◽  
Minimum Phase ◽  
Predictive Deconvolution ◽  
Different Levels ◽  
Zero Phase

Predictive deconvolution is commonly applied to seismic data generated with a Vibroseisr® source. Unfortunately, when this process invokes a minimum‐phase assumption, the phase of the resulting trace will not be correct. Nonetheless, spiking deconvolution is an attractive process because it restores attenuated higher frequencies, thus increasing resolution. For detailed stratigraphic analyses, however, it is desirable that the phase of the data be treated properly as well. The most common solution is to apply a phase‐shifting filter that corrects for errors attributable to a zero‐phase source. The phase correction is given by the minimum‐phase spectrum of the correlated Vibroseis wavelet. Because no minimum‐phase spectrum truly exists for this bandlimited wavelet, white noise is added to its amplitude spectrum in order to design the phase‐correction filter. Different levels of white noise, however, produce markedly different results when field data sections are filtered. A simple argument suggests that the amount of white noise used should match that added in designing the (minimum‐phase) spiking deconvolution operator. This choice, however, also produces inconsistent results; field data again show that the phase treatment is sensitive to the amount of added white noise. Synthetic data tests show that the standard phase‐correction procedure breaks down when earth attenuation is severe. Deterministically reducing the earth‐filter effects before deconvolution improved the resulting phase treatment for the synthetic data. After application of the inverse attenuation filter to the field data, however, phase differences again remain for different levels of added white noise. These inconsistencies are attributable to the phase action of spiking deconvolution. This action is dependent upon the shape of the signal spectrum as well as the spectral shape and level of contaminating noise. Thus, in practice the proper treatment of phase in data-dependent processing requires extensive knowledge of the spectral characteristics of both signal and noise. With such knowledge, one could apply deterministic techniques that either eliminate the need for statistical deconvolution or condition the data so as to satisfy better the statistical model assumed in data‐dependent processing.

Phase Correction of Vibrational Circular Dichroic Features

1988 ◽  
Vol 42 (2) ◽  
pp. 336-341 ◽  
Author(s):  
Colleen A. McCoy ◽  
James A. De Haseth
Keyword(s):  
Phase Error ◽  
Absorption Spectrometry ◽  
Phase Correction ◽  
Phase Curve ◽  
Phase Retardation ◽  
Normal Absorption ◽  
Ft Ir ◽  
Arctangent Function ◽  
Circular Dichroic ◽  
Zero Phase

Several sources of phase-correction-induced spectral anomalies in FT-IR vibrational circular dichroism (VCD) spectra have been investigated. Misidentification of the zero-phase retardation position in dichroic interferograms that exhibit no optical or electronic bias can produce spectral errors. Production of such errors is from the introduction of linear phase error into the phase curve. When the zero-phase retardation position is correctly identified, other spectral anomalies, such as “reflected peaks,” can appear in VCD spectra. These peaks are readily observed in quarterwave plate reference spectra. The anomalies are directly correlated to the arctangent function used to define the phase curve and result only from the nature of the VCD signal. VCD spectra can exhibit negative, as well as positive, peaks; consequently the phase correction must be designed to accommodate negative features. Both Mertz and Forman phase-correction algorithms have been modified to correct the phase of VCD interferograms without error. Such corrections are not necessary, or even desirable, for normal absorption spectrometry.

Automatic phase correction of common‐midpoint stacked data

Geophysics ◽  
1987 ◽  
Vol 52 (1) ◽  
pp. 51-59 ◽  
Author(s):  
S. Levy ◽  
D. W. Oldenburg
Keyword(s):  
Phase Correction ◽  
Well Logs ◽  
Seismic Section ◽  
Measured Velocity ◽  
Residual Phase ◽  
Automatic Phase ◽  
Frequency Independent ◽  
Independent Constant ◽  
Non Gaussian ◽  
Zero Phase

The residual wavelet on a processed seismic section is often not zero phase despite all efforts to make it so. In this paper we adopt the convolutional model for the processed seismogram, assume that the residual phase shift can be approximated by a frequency‐independent constant, and use the varimax norm to generate an algorithm to estimate the residual phase directly. Application of our algorithm to reflectivities from well logs suggests that it should work in the majority of cases so long as the reflectivity is non‐Gaussian. An application of our algorithm to stacked data enhances the interpretability of the seismic section and leads to an improved match between the recovered relative acoustic impedance and a measured velocity log.

Predictive deconvolution in the frequency domain

1988 ◽  
Author(s):  
Tad J. Ulrych ◽  
Milton Porsani ◽  
Jacob T. Fokkema ◽  
W. Scott ◽  
P. Leaney ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Predictive Deconvolution
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