1887

Abstract

Summary

Time-frequency decomposition has been widely used in seismic data analysis. The short-time Fourier transform and wavelet transform are the popular methods to decompose a signal from time domain to time-frequency domain. However, the application of both approaches is restricted due to trade-offs between time and frequency resolution. In this paper, a new time-frequency decomposition approach based on synchrosqueezing transform (SST) is presented, which has a firm mathematical foundation and produces the improved time-frequency resolution. A non-stationary synthetic example shows that the SST can achieve higher resolution both in time and frequency compared with continuous wavelet transform (CWT). Field data examples demonstrate the potential of the SST in detecting low-frequency anomaly caused by hydrocarbon, which is helpful to delineate the location and extent of anomaly more clearly.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201412820
2015-06-01
2020-04-03
Loading full text...

Full text loading...

References

  1. SinhaS., RouthP. S., AnnoP. and CastagnaJ. P.
    [2005] Spectral decomposition of seismic data with continuous wavelet transform. Geophysics, 70, P19–P25.
    [Google Scholar]
  2. StockwellR. G., MansinhaL. and LoweR. P.
    [1996] Localization of the complex spectrum: The S transform. IEEE Transactions on Signal Processing, 44, 998–1001.
    [Google Scholar]
  3. Jeffrey. C. and William, J.
    [1999] On the existence of discrete Wigner distributions. IEEE Signal Processing Letters, 6, 304–306.
    [Google Scholar]
  4. Wang, Y.
    [2007] Seismic time-frequency spectral decomposition by matching pursuit. Geophysics, 72, V13–V20.
    [Google Scholar]
  5. BonarD. C. and M. D.Sacchi
    [2010] Complex spectral decomposition via inversion strategies. 80th SEG Annual International Meeting, Expanded Abstracts, 1408–1412.
    [Google Scholar]
  6. DaubechiesI., J.Lu and H. T.Wu
    [2011] Synchrosqueezed wavelet transform: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30, 243–261.
    [Google Scholar]
  7. ThakurG., E.Brevdo, N. S.Fuckar and H.T.Wu
    [2013] The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and paleoclimate applications. Signal Processing, 93, 1079–1094.
    [Google Scholar]
  8. HerreraR. H., HanJ. and M.van der Baan
    [2014] Applications of the synchrosqueezing transform in seismic time-frequency analysis. Geophysics, 79, V55–V64.
    [Google Scholar]
  9. Daubechies, I. and S.Maes
    [1996] A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. Wavelets in Medicine and Biology. Boca Raton, FL, USA: CRC Press, 527–546.
    [Google Scholar]
  10. Li, C. and M.Liang
    [2012] Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrossqueezing transform. Mechanical Systems and Signal Processing, 26, 205–217.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201412820
Loading
/content/papers/10.3997/2214-4609.201412820
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error