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Seismic time–frequency spectrum analysis based on local polynomial Fourier transform

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Time–frequency analysis technology is widely used in non-stationary seismic data analysis. The energy concentration of the spectrum depends on the consistency of the kernel function of the time–frequency analysis method and the instantaneous frequency variation of the signals. The conventional time–frequency analysis methods usually require that the local instantaneous frequency of the signals remains unchanged or linearly changed. So it is difcult to accurately characterize the instantaneous frequency nonlinear variation of the non-stationary signal. The local polynomial Fourier transform (LPFT) method can efectively describe the instantaneous frequency variation by local high-order polynomial ftting and obtain the results with high spectral and energy concentration. The numerical simulations and feld seismic data applications show that the time–frequency spectrum results obtained by LPFT can refect the instantaneous frequency variation characteristics of the seismic data, while ensuring the concentration of time–frequency energy.
Czasopismo
Rocznik
Strony
1--17
Opis fizyczny
Bibliogr. 37 poz.
Twórcy
autor
  • School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
autor
  • School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Bibliografia
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  • 7. Li X, Bi G, Stankovic S, Zoubir AM (2011) Local polynomial Fourier transform: a review on recent developments and applications. Signal Process 91(6):1370–1393
  • 8. Li D, Castagna JP, Goloshubin G (2016) Investigation of generalized S-transform analysis windows for time–frequency analysis of seismic reflection data. Geophysics 81(3):V235–V247
  • 9. Liu J, Marfurt KJ (2007) Instantaneous spectral attributes to detect channels. Geophysics 72(2):P23–P31
  • 10. Liu NH, Gao JH, Jiang XD, Zhang ZS, Wang Q (2016) Seismic time–frequency analysis via STFT-based concentration of frequency and time. IEEE Geosci Remote Sens Lett 14(1):127–131
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  • 18. Puryear CI, Portniaguine ON, Cobos CM, Castagna JP (2012) Constrained least-squares spectral analysis: application to seismic data. Geophysics 77(5):V143–V167
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  • 23. Sun S, Castagna JP, Siegfried RW (2002) Examples of wavelet transform time–frequency analysis in direct hydrocarbon detection. In: SEG technical program expanded abstracts, pp 457–460
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  • 28. Wang YH (2010) Multichannel matching pursuit for seismic trace decomposition. Geophysics 75(4):V61–V66
  • 29. Wang LL, Gao JH, Zhao W, Jiang XD (2012) Enhancing resolution of nonstationary seismic data by molecular-gabor transform. Geophysics 78(1):V31–V41
  • 30. Wang TY, Yuan SY, Gao JH, Li SJ, Wang SX (2019) Multispectral phase-based geosteering coherence attributes for deep stratigraphic feature characterization. IEEE Geosci Remote Sens Lett 16(8):1309–1313
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  • 33. Yu G, Zhou YQ (2016) General linear chirplet transform. Mech Syst Signal Process 70:958–973
  • 34. Yuan SY, Wang SX, Ma M, Ji YZ, Deng L (2017) Sparse Bayesian learning-based time-variant deconvolution. IEEE Trans Geosci Remote Sens 55(11):6182–6194
  • 35. Yuan SY, Ji YZ, Shi PD, Zeng J, Gao JH, Wang SX (2019a) Sparse Bayesian learning-based seismic high-resolution time–frequency analysis. IEEE Geosci Remote Sens Lett 16(4):623–627
  • 36. Yuan SY, Liu Y, Zhang Z, Luo CM, Wang SX (2019b) Prestack stochastic frequency-dependent velocity inversion with rock-physics constraints and statistical associated hydrocarbon attributes. IEEE Geosci Remote Sens Lett 16(1):140–144
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-428bf799-9b9d-4e53-9820-9cb73b1b6f02
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