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Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution?

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Title: Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution?
Authors: van der Baan, Mirko1 Mirko.vanderBaan@ualberta.ca
Source: Geophysics. Jul/Aug2012, Vol. 77 Issue 4, pV133-V142. 10p.
Subjects: Deconvolution in seismic reflection, Seismic reflection method data processing, Dispersion (Chemistry), Attenuation (Physics), Bandwidths, Estimation theory
Abstract: Dispersion and attenuation corrections can improve the resolution of seismic data. This significantly facilitates interpretation. In principle, inverse Q filtering and the time-varying Wiener deconvolution can achieve this. Inverse Q filtering is a deterministic process that requires knowledge of the quality factor Q, whereas the time-varying Wiener deconvolution is a statistical approach based on the estimation of the nonstationary propagating wavelet. Dispersion corrections based on phase-only inverse Q filtering is an inherently stable method that is robust in the presence of noise. Attenuation corrections via amplitude-only inverse Q filtering, on the other hand, is likely to lead to noise amplification as well as bandwidth enhancement. Dispersion corrections via the time-varying Wiener deconvolution are challenging because these require estimation of a nonstationary, frequency-dependent, nonminimum-phase wavelet. Fortunately, attenuation corrections via the Wiener deconvolution need only estimation of a zero-phase time-varying wavelet for which robust methods exist. The most promising procedure for combined dispersion and attenuation correction is thus comprised of first applying dispersion corrections using phase-only inverse Q filtering, followed by zero-phase time-varying Wiener deconvolution. [ABSTRACT FROM AUTHOR]
Copyright of Geophysics is the property of Society of Exploration Geophysicists and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution?
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  Data: <searchLink fieldCode="AR" term="%22van+der+Baan%2C+Mirko%22">van der Baan, Mirko</searchLink><relatesTo>1</relatesTo><i> Mirko.vanderBaan@ualberta.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Geophysics%22">Geophysics</searchLink>. Jul/Aug2012, Vol. 77 Issue 4, pV133-V142. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Deconvolution+in+seismic+reflection%22">Deconvolution in seismic reflection</searchLink><br /><searchLink fieldCode="DE" term="%22Seismic+reflection+method+data+processing%22">Seismic reflection method data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Dispersion+%28Chemistry%29%22">Dispersion (Chemistry)</searchLink><br /><searchLink fieldCode="DE" term="%22Attenuation+%28Physics%29%22">Attenuation (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Bandwidths%22">Bandwidths</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink>
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  Data: Dispersion and attenuation corrections can improve the resolution of seismic data. This significantly facilitates interpretation. In principle, inverse Q filtering and the time-varying Wiener deconvolution can achieve this. Inverse Q filtering is a deterministic process that requires knowledge of the quality factor Q, whereas the time-varying Wiener deconvolution is a statistical approach based on the estimation of the nonstationary propagating wavelet. Dispersion corrections based on phase-only inverse Q filtering is an inherently stable method that is robust in the presence of noise. Attenuation corrections via amplitude-only inverse Q filtering, on the other hand, is likely to lead to noise amplification as well as bandwidth enhancement. Dispersion corrections via the time-varying Wiener deconvolution are challenging because these require estimation of a nonstationary, frequency-dependent, nonminimum-phase wavelet. Fortunately, attenuation corrections via the Wiener deconvolution need only estimation of a zero-phase time-varying wavelet for which robust methods exist. The most promising procedure for combined dispersion and attenuation correction is thus comprised of first applying dispersion corrections using phase-only inverse Q filtering, followed by zero-phase time-varying Wiener deconvolution. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Geophysics is the property of Society of Exploration Geophysicists and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1190/GEO2011-0500.1
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        Text: English
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        StartPage: V133
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      – SubjectFull: Deconvolution in seismic reflection
        Type: general
      – SubjectFull: Seismic reflection method data processing
        Type: general
      – SubjectFull: Dispersion (Chemistry)
        Type: general
      – SubjectFull: Attenuation (Physics)
        Type: general
      – SubjectFull: Bandwidths
        Type: general
      – SubjectFull: Estimation theory
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      – TitleFull: Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution?
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              M: 07
              Text: Jul/Aug2012
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