An Amplitude- and Frequency- Preserving S Transform

The time–frequency analysis is very useful for attenuation compensation, quality factor Q estimation, anomaly detection, and so on. Among plenty of time–frequency analysis methods, the S transform (ST) and its extensions are widely used because of their self-adjustable flexibility, compared with the short-time Fourier transform and Gabor transform. However, the traditional ST has a poor amplitude-preserving property near the boundary while being implemented in the time domain, because the partition of unity cannot be guaranteed. Besides, the frequency distribution biases the actual Fourier spectrum because of the linear-frequency-dependent term in the analytical window, which can decrease the accuracy of attenuation estimation. To preserve the amplitude and frequency, a new analytical window is designed, and the corresponding comprehensive window is derived in the time domain. The frequency-domain formulae are derived in detail for an efficient implementation, in which the time-domain convolution is achieved through multiplication. Numerical examples on the synthetic layered model and pseudorandom time series demonstrate the validity of the proposed method in amplitude- and frequency-preserving quantitatively. Examples at a well location of field data further demonstrate its frequency-preserving property qualitatively. Furthermore, the proposed method can have wide applications in exploration geophysics, seismology, or signal analysis fields, combining with the synchrosqueezing transform.

[1]  Juan José Dañobeitia,et al.  The $S$-Transform From a Wavelet Point of View , 2008, IEEE Transactions on Signal Processing.

[2]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[3]  C. Robert Pinnegar,et al.  The S-transform with windows of arbitrary and varying shape , 2003 .

[4]  Jinghuai Gao,et al.  Seismic Time–Frequency Analysis via STFT-Based Concentration of Frequency and Time , 2017, IEEE Geoscience and Remote Sensing Letters.

[5]  Qian Wang,et al.  Time–Frequency Analysis of Seismic Data Using a Three Parameters S Transform , 2018, IEEE Geoscience and Remote Sensing Letters.

[6]  Benfeng Wang,et al.  An Efficient Amplitude-Preserving Generalized S Transform and Its Application in Seismic Data Attenuation Compensation , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Xiaotao Wen,et al.  Q estimation of seismic data using the generalized S-transform , 2016 .

[8]  Jingye Li,et al.  Amplitude variation with offset inversion using the reflectivity method , 2016 .

[9]  David C. Henley,et al.  Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data , 2011 .

[10]  Wei Liu,et al.  Spectral Decomposition for Hydrocarbon Detection Based on VMD and Teager–Kaiser Energy , 2017, IEEE Geoscience and Remote Sensing Letters.

[11]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[12]  Yanghua Wang,et al.  Q analysis on reflection seismic data , 2004 .

[13]  John P. Castagna,et al.  S-transform and Fourier transform frequency spectra of broadband seismic signals , 2017 .

[14]  Wei Liu,et al.  Seismic Time–Frequency Analysis via Empirical Wavelet Transform , 2016, IEEE Geoscience and Remote Sensing Letters.

[15]  Jing-Hua Gao,et al.  Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Transform , 2014, IEEE Geoscience and Remote Sensing Letters.

[16]  Wagner Moreira Lupinacci,et al.  A combined time-frequency filtering strategy for Q-factor compensation of poststack seismic data , 2017 .

[17]  R. P. Lowe,et al.  Pattern analysis with two-dimensional spectral localisation: Applications of two-dimensional S transforms , 1997 .

[18]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[19]  Benfeng Wang An Amplitude Preserving S-Transform for Seismic Data Attenuation Compensation , 2016, IEEE Signal Processing Letters.

[20]  Zhenming Peng,et al.  Matching Pursuit-Based Sliced Wigner Higher Order Spectral Analysis for Seismic Signals , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.