Application of synchrosqueezed wave packet transform in high-resolution seismic time-frequency analysis
Wang, Q. and Gao, J., 2017. Application of synchrosqueezed wave packet transform in high resolution seismic time-frequency analysis. Journal of Seismic Exploration, 26: 587-599. Time-frequency (T-F) representation is a cornerstone in the seismic data processing and interpretation. It reveals the local frequency information that is hidden in the Fourier spectrum. The high resolution of the T-F representation is of great significance in depicting subtle geologic structures and in detecting anomalies associated with hydrocarbon reservoirs. The traditional T-F representations include short-time Fourier transform (STFT), continuous wavelet transform (CWT), S-transform (ST) and Wigner-Ville distribution (WVD). However, due to the uncertainty principle and cross-term, these methods suffer from low time-frequency resolution. In this paper, we introduce a new methodology for obtaining a high-quality T-F representation which is termed the synchrosqueezed wave packet transform (SSWPT). It is the first time that SSWPT is applied to multichannel seismic data time-frequency analysis. The SSWPT is a promising tool to provide detailed T-F representation. We validate the proposed approach with a synthetic example and compare the result with existing methods. Two field examples illustrate the effectiveness of SSWPT to identify subtle stratigraphic features for reservoir characterization.
- Castagna, J.P., S. Sun and R.W. Seigfried, 2003. Instantaneous spectral analysis:
- Detection of low-frequency shadows associated with hydrocarbon. The Leading
- Edge, 22: 120-127.
- Chakraborty, A. and Okaya, D., 1995. Frequency-time decomposition on seismic data
- using wavelet-based methods. Geophysics, 60: 1906-1916.
- Cohen, L., 1989. Time-frequency distributions-Review. Proc. IEEE., 77: 941-981.
- Daubechies, I., Lu, J. and Wu, H., 2011. Synchrosqueezed wavelet transform: An
- empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30: 243-
- Han, J. and van der Baan, M., 2013. Empirical mode decomposition for seismic time-
- frequency analysis. Geophysics, 78: 09-019.
- Herrera, R.H., Han, J. and van der Baan, M., 2014. Application of synchrosqueezing transform in
- seismic time-frequency analysis. Geophysics, 79: V55-V64.
- Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N-C., Tung,
- C.C. and Liu, H.H., 1998. The empirical mode decomposition and the Hilbert
- spectrum for nonlinear and nonstationary time series analysis. Proc. Roy. Soc.
- Lond. A, 454: 903-995.
- Jeffrey, C. and William, J., 1999. On the existence of discrete Wigner distributions. IEEE
- Signal Proc. Lett., 6: 304-306.
- Li, C. and Liang M., 2012. A generalized synchrosqueezing transform for enhancing
- signal time-frequency representation. Signal Process., 92: 2264-2274.
- Li, D., Castagna, J. and Golushubin., G., 2016. Investigation of generalized S-transform
- analysis windows for time-frequency analysis of seismic reflection data.
- Geophysics, 81: V235-V247.
- Mallat, S. and Zhang, Z., 1993. Matching persuit with time-frequency dictionary. IEEE
- Transact. Signal Process., 41: 3397-3415.
- Oberlin, T., Meignen, S. and Perrier, V., 2014. The Fourier-based synchrosqueezing
- transform. ICASSP, Italy: 315-319.
- Sinha, S., Routh, P.S., Anno, P.D. and Castagna, J.P., 2005. Spectral decomposition of
- seismic data with continuous-wavelet transform. Geophysics, 70: 19-25.
- Stockwell, R., Mansinha, L. and Lowe, R., 1996. Localization of complex spectrum: The
- S transform. IEEE Trans. Sign. Process., 44: 998-1001.
- Wang, P., Gao, J. and Wang, Z., 2014. Time-frequency analysis of seismic data using
- synchrosqueezing transform. IEEE Geosci. Remote Sens. Lett., 11: 2042-2044.
- Yang, H., 2014. Synchrosqueezed wave packet transforms and diffeomorphism based
- spectral analysis for 1D general mode decompositions. Appl. Comput. Harmon.
- Anal., 39: 33-66.
