Horizontal reassignment synchrosqueezing transform for time-frequency analysis of seismic data
Liu, W., Zhang, S.Y., Chen, K.F. and Li, S.X., 2022. Horizontal reassignment synchrosqueezing transform for time-frequency analysis of seismic data. Journal of Seismic Exploration, 31: 325-339. The short-time Fourier transform (STFT)-based synchrosqueezing transform (FSST) is a special type of reassignment method that achieves a compact time-frequency representation (TFR) for a class of nonstationary signal. However, for the signals with a strongly varying instantaneous frequency, the FSST method is always not desirable. To address the problem, a new method, termed as horizontal reassignment synchrosqueezing transform (HRSST), is proposed in the paper. By means of an unbiased group delay (GD) estimation, the HRSST provides a sharped TFR for transient signals in which the time-frequency ridge is nearly parallel with frequency axis. Through synthetic data, the proposed HRSST method is determined to be an effective and robust tool which provides superior results over some classical TFA techniques such as STFT and FSST. Finally, two field examples are employed to further demonstrate its potential in time localization characterization and subsurface geological structures delineation with high precision.
- Allen, J.B., 1977. Short term spectral analysis, synthetic and modification by discrete
- Fourier transform. IEEE Transact. Acoust., Speech, Sign. Process., 25: 235-238.
- Auger, F. and Flandrin, P., 1995. Improving the readability of time-frequency and
- time-scale representations by the reassignment method. IEEE Transact. Sign.
- Process., 43: 1068-1089.
- Castagna, J.P., Sun, S. and Siegfried, R.W., 2003. Instantaneous spectral analysis:
- detection of low-frequency shadows associated with hydrocarbons. The Leading
- Edge, 22: 120-127.
- Chen, Y., Liu, T., Chen, X., Li, J. and Wang, E., 2014. Time-frequency analysis of
- seismic data using synchrosqueezing wavelet transform. J. Seismic Explor., 23:
- 303-312.
- Daubechies, I., Lu, J. and Wu., H.T., 2011. Synchrosqueezed wavelet transform: An
- empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30:
- 243-261.
- Daubechies, I. and Maes, S., 1996. A nonlinear squeezing of the continuous wavelet
- transform based on auditory nerve models. In: Aldroubi, A. and Unser, M. (Eds.),
- Wavelets in Medicine and Biology. CRC Press, Houston: 527-546.
- Herrera, R.H., Han, J. and van der Baan, M., 2014, Applications of the synchrosqueezing
- transform in seismic time-frequency analysis. Geophysics, 79(3): V55-V64.
- Jeffrey, C. and William, J., 1999. On the existence of discrete Wigner distributions.
- IEEE Sign. Process. Let., 6: 304-306.
- Liu, W., Chen W. and Zhang Z. 2020. A novel fault diagnosis approach for rolling
- bearing based on high-order synchrosqueezing transform. IEEE Access, 8:
- 12533-12541.
- Liu, W. and Chen, W., 2019. Recent advancements in empirical wavelet transform and
- its applications. IEEE Access, 7: 103770-103780.
- Liu, W. and Duan, Z., 2020. Seismic signal denoising using fx variational mode
- decomposition. IEEE Geosci. Remote Sens. Lett., 17: 1313-1317.
- Liu, Y. and Fomel, S., 2013. Seismic data analysis using local time-frequency
- decomposition. Geophys. Prospect., 61: 516-525.
- Oberlin, T., Meignen, S., and Perrier, V., 2015. Second-order synchrosqueezing
- transform or invertible reassignment? Towards ideal time-frequency representations.
- IEEE Transact. Sign. Process., 63: 1335-1344.
- Pham, D.H. and Meignen, S., 2017. High-order synchrosqueezing transform for
- multicomponent signals analysis - With an application to gravitational-wave signal.
- IEEE Transact. Sign. Process., 65: 3168-3178.
- Sinha, S., Routh, P., Anno, P. and Castagna, J., 2005. Spectral decomposition of seismic
- data with continuous-wavelet transform. Geophysics, 70(6): 19-25.
- Thakur, G. and Wu, H.T., 2011. Synchrosqueezing-based recovery of instantaneous
- frequency from nonuniform samples. SIAM J. Math. Anal., 43: 2078-2095.
