Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

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

Warianty tytułu
Języki publikacji
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.
Opis fizyczny
Bibliogr. 37 poz.
  • School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
  • School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
  • 1. Castagna JP, Sun SJ, Siegfried RW (2003) Instantaneous spectral analysis: detection of low-frequency shadows associated with hydrocarbons. Lead Edge 22(2):120–127
  • 2. Cohen L (1995) Time–frequency analysis. Prentice Hall, Upper Saddle River
  • 3. Durak L, Arikan O (2003) Short-time Fourier transform: two fundamental properties and an optimal implementation. IEEE Trans Signal Process 51(5):1231–1242
  • 4. Gribonval R (2001) Fast matching pursuit with a multiscale dictionary of Gaussian chirps. IEEE Trans Signal Process 49(5):994–1001
  • 5. Kadambe S, Boudreaux-Bartels GF (1992) A comparison of the existence of ‘cross terms’ in the Wigner distribution and the squared magnitude of the wavelet transform and the short-time Fourier transform. IEEE Trans Signal Process 40(10):2498–2517
  • 6. Katkovnik V (1998) Discrete-time local polynomial approximation of the instantaneous frequency. IEEE Trans Signal Process 46(10):2626–2637
  • 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
  • 11. Mallat SG, Zhang Z (1993) Matching pursuits with time–frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415
  • 12. Mann S, Haykin S (1995) The chirplet transform: physical considerations. IEEE Trans Signal Process 43(11):2745–2761
  • 13. Naseer MT, Asim S (2017) Detection of cretaceous incised-valley shale for resource play, Miano gas field, SW Pakistan: spectral decomposition using continuous wavelet transform. J Asian Earth Sci 147:358–377
  • 14. Ouadfeul SA, Aliouane L (2014) Random seismic noise attenuation data using the discrete and the continuous wavelet transforms. Arab J Geosci 7(7):2531–2537
  • 15. Parolai S (2009) Denoising of seismograms using the S transform. Bull Seismol Soc Am 99(1):226–234
  • 16. Partyka G, Gridley J, Lopez J (1999) Interpretational applications of spectral decomposition in reservoir characterization. Lead Edge 18(3):353–360
  • 17. Phinyomark A, Limsakul C, Phukpattaranont P (2011) Application of wavelet analysis in EMG feature extraction for pattern classification. Meas Sci Rev 11(2):45–52
  • 18. Puryear CI, Portniaguine ON, Cobos CM, Castagna JP (2012) Constrained least-squares spectral analysis: application to seismic data. Geophysics 77(5):V143–V167
  • 19. Radad M, Gholami A, Siahkoohi HR (2015) S-transform with maximum energy concentration: application to non-stationary seismic deconvolution. J Appl Geophys 100(118):155–166
  • 20. Rene RM, Fitter JL, Forsyth PM, Kim KY, Murray DJ, Walters JK, Westerman JD (1986) Multicomponent seismic studies using complex trace analysis. Geophysics 51(6):1235–1251
  • 21. Sinha S, Routh P, Anno P (2009) Instantaneous spectral attributes using scales in continuous-wavelet transform. Geophysics 74(2):WA137–WA142
  • 22. Smith M, Perry G, Stein J, Bertrand A, Yu G (2008) Extending seismic bandwidth using the continuous wavelet transform. First Break 26(6):97–102
  • 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
  • 24. Terrien J, Marque C, Germain G (2008) Ridge extraction from the time–frequency representation (TFR) of signals based on an image processing approach: application to the analysis of uterine electromyogram AR TFR. IEEE Trans Biomed Eng 55(5):1496–1503
  • 25. Ville J (1948) Theorie et application de la notion de signal analytic. Cables Transm 93:61–74
  • 26. Wang YH (2004) Q analysis on reflection seismic data. Geophys Res Lett 31:L17606
  • 27. Wang YH (2006) Inverse Q-filter for seismic resolution enhancement. Geophysics 71(3):V51–V60
  • 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
  • 31. Wu L, Castagna JP (2017) S-transform and Fourier transform frequency spectra of broadband seismic signals. Geophysics 82(5):O71–O81
  • 32. Yang Y, Peng ZK, Dong XJ, Zhang WM, Meng G (2014) General parameterized time–frequency transform. IEEE Trans Signal Process 62(11):2751–2764
  • 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
  • 37. Zhong JG, Huang Y (2010) Time–frequency representation based on an adaptive short-time Fourier transform. IEEE Trans Signal Process 58(10):5118–5128
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.