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Volume 70, Issue 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Owing to the influence of absorption and scattering, the components of seismic waves, especially those with higher frequencies, always suffer amplitude attenuation and phase distortion during their propagation through the earth. Nonstationary blind deconvolution takes such attenuations into account for prestack seismic data. This not only compensates for attenuation‐induced energy loss but also simultaneously addresses the energy loss associated with the reflectivity series and wavelet. Additionally, nonstationary blind deconvolution can greatly improve the resolution of seismic data. However, prestack nonstationary blind deconvolution is difficult to implement because it is not easy to estimate the attenuation function of prestack seismic data unless the quality factor of the Earth is assumed to be constant. In this regard, we introduced a nonstationary blind deconvolution method for prestack gathering. First, we used warped mapping to calculate the attenuation function of the prestack data. Then, we incorporated the attenuation function into the nonstationary sparse spike deconvolution based on Toeplitz‐sparse matrix factorization for reflectivity series and wavelet estimation. The proposed method is velocity‐independent and can be adapted to the time‐varying Q model. To validate its stability and effectiveness, numerical and real data examples were adopted. The results show that the proposed method provides a straightforward routine for the estimation of prestack reflectivity coefficients for further processing and inversion.

© 2022 European Association of Geoscientists & Engineers © 2022 European Association of Geoscientists & Engineers
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2022-04-14
2024-04-26

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Warped mapping–based blind deconvolution for resolution improvement
Geophysical Prospecting 70, 677 (2022); https://doi.org/10.1111/1365-2478.13186
/content/journals/10.1111/1365-2478.13186
/content/journals/10.1111/1365-2478.13186
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  • Article Type: Research Article
Keyword(s): attenuation; Inverse problem; Seismic; Signal processing

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