Investigation of generalized S-transform analysis windows for time-frequency analysis of seismic reflection data

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V235-V247 ◽  
Author(s):  
Duan Li ◽  
John Castagna ◽  
Gennady Goloshubin
Keyword(s):  
Temporal Resolution ◽  
Seismic Reflection ◽  
Frequency Resolution ◽  
Low Frequencies ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Gaussian Window ◽  
Reflection Data ◽  
S Transform ◽  
Generalized S Transform

The frequency-dependent width of the Gaussian window function used in the S-transform may not be ideal for all applications. In particular, in seismic reflection prospecting, the temporal resolution of the resulting S-transform time-frequency spectrum at low frequencies may not be sufficient for certain seismic interpretation purposes. A simple parameterization of the generalized S-transform overcomes the drawback of poor temporal resolution at low frequencies inherent in the S-transform, at the necessary expense of reduced frequency resolution. This is accomplished by replacing the frequency variable in the Gaussian window with a linear function containing two coefficients that control resolution variation with frequency. The linear coefficients can be directly calculated by selecting desired temporal resolution at two frequencies. The resulting transform conserves energy and is readily invertible by an inverse Fourier transform. This modification of the S-transform, when applied to synthetic and real seismic data, exhibits improved temporal resolution relative to the S-transform and improved resolution control as compared with other generalized S-transform window functions.

scholarly journals Time-Frequency Domain Deconvolution based on Synchrosqueezing Generalized S Transform

2021 ◽  
pp. 148-155
Author(s):  
Shulin Zheng ◽  
Zijun Shen
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Oil And Gas ◽  
Amplitude Spectrum ◽  
Frequency Resolution ◽  
Good Effect ◽  
Time Frequency ◽  
S Transform ◽  
Convolution Model ◽  
Generalized S Transform

Complex geological characteristics and deepening of the mining depth are the difficulties of oil and gas exploration at this stage, so high-resolution processing of seismic data is needed to obtain more effective information. Starting from the time-frequency analysis method, we propose a time-frequency domain dynamic deconvolution based on the Synchrosqueezing generalized S transform (SSGST). Combined with spectrum simulation to estimate the wavelet amplitude spectrum, the dynamic convolution model is used to eliminate the influence of dynamic wavelet on seismic records, and the seismic signal with higher time-frequency resolution can be obtained. Through the verification of synthetic signals and actual signals, it is concluded that the time-frequency domain dynamic deconvolution based on the SSGST algorithm has a good effect in improving the resolution and vertical resolution of the thin layer of seismic data.

Broadband seismic data — The importance of low frequencies

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. WA3-WA14 ◽  
Author(s):  
Fons ten Kroode ◽  
Steffen Bergler ◽  
Cees Corsten ◽  
Jan Willem de Maag ◽  
Floris Strijbos ◽  
...  
Keyword(s):  
Research And Development ◽  
Experimental Evidence ◽  
Case Studies ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Low Frequencies ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Enhanced Resolution ◽  
Impedance Inversion

We considered the importance of low frequencies in seismic reflection data for enhanced resolution, better penetration, and waveform and impedance inversion. We reviewed various theoretical arguments underlining why adding low frequencies may be beneficial and provided experimental evidence for the improvements by several case studies with recently acquired broadband data. We discussed where research and development efforts in the industry with respect to low frequencies should be focusing.

scholarly journals Improving the interpretability of air-gun seismic reflection data using deterministic filters: A case history from offshore Cape Leeuwin, southwest Australia

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. B113-B125 ◽  
Author(s):  
Colin Sargent ◽  
Richard W. Hobbs ◽  
Darren R. Gröcke
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Amplitude Spectrum ◽  
Pulse Signal ◽  
Black Shales ◽  
Low Frequencies ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Integrated Ocean Drilling Program ◽  
Air Gun

To identify drilling targets for an Integrated Ocean Drilling Program project to investigate high-latitude black shales required reinterpretation of legacy seismic data. The original processing had identified the major structures but was of insufficient resolution to map the more-subtle markers at the top of the shale sequence. By reprocessing these 2004 vintage 2D air-gun marine seismic reflection data we show that the application of filters determined from deepwater data yields subbottom geological imaging superior to statistical methods and arguably better than modeled source deconvolution methods, particularly for recovery of low frequencies. The data were acquired to the southwest of Australia in an area with swells that are typically 2–4 m and cause distortions to the predicted source and receiver response functions. These distortions cannot be incorporated in an idealized modeled source function; hence, we have opted to design the deterministic filters from the seismic data. We applied the deconvolution in two steps: a prestack filter to suppress the air-gun bubble pulse signal and a poststack filter to suppress the notches in the amplitude spectrum caused by the free-surface reflections at the source and the receiver. Through this strategy, we expanded the seismic data bandwidth at the low and high frequencies and improved resolution. The tie with the single borehole in the area was significantly improved and has enabled a more-confident interpretation of the shale horizons.

Time Frequency Wavenumber Analysis of Surface Waves and Signal Enhancement Using S-Transform

2015 ◽  
Vol 23 (04) ◽  
pp. 1540010
Author(s):  
Zhao Zhang ◽  
Yuefeng Sun ◽  
Karl Berteussen
Keyword(s):  
Surface Waves ◽  
Low Frequency ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Reflection Data ◽  
Time Space ◽  
S Transform ◽  
Variable Factor ◽  
Over Time

Highly dispersive surface waves resulting from the combination of strong lateral seafloor heterogeneities, shallow water depths and hard sea bottom severely degrade seismic reflection data quality. Considering that seismic signals are nonstationary and surface waves have various spectra over time, we proposed S-transform-based time frequency wavenumber analysis technique which allows the dynamic analysis of spectrum over time. The data is first transformed from the time-space domain to the time-wavenumber domain through one-dimensional Fourier transform over the spatial variable, then the variable-factor S-transform is applied over time. Nonstationary filtering is then designed to identify and separate surface waves in complex wavefields, based on its low frequency and low velocity properties, in the time-frequency-wavenumber (TFK) domain. Application to field data illustrates that with this technique, not only is the surface wave effectively suppressed, but also the reflective signals are enhanced, which confirm the validity of the method.

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

A Time-Frequency Filtering Method Based on Generalized S Transform and its Application in Machinery Fault Diagnosis

2012 ◽  
Vol 157-158 ◽  
pp. 531-537 ◽  
Author(s):  
Xiu Wen Li ◽  
Jian Hong Yang ◽  
Min Li ◽  
Jin Wu Xu
Keyword(s):  
Frequency Resolution ◽  
Good Effect ◽  
Frequency Filtering ◽  
Cross Term ◽  
Signal Energy ◽  
Time Frequency ◽  
Filtering Method ◽  
S Transform ◽  
Generalized S Transform ◽  
Machinery Fault Diagnosis

Aimed at the problem of low resolution and cross term interference of the traditional time-frequency analysis methods, a new time-frequency filtering method based on generalized S transform is proposed. The method is extended under the premise of the linearity, lossless invertibility, high time-frequency resolution of S transform. On the basis, a coefficient which is direct to the signal energy distribution is introduced. In this way, the resolution of the S transform can be adjust adaptively. Eventually, this method is applied to the time-frequency filtering. The results of simulation and faulty bearing show that the proposed methodology can achieve good effect of noise reduction, and be more suitable for the non-stationary characteristics of vibration signals.

Estimation of seismic quality factor in the time-frequency domain using variational mode decomposition

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. V329-V343
Author(s):  
Ya-Juan Xue ◽  
Jun-Xing Cao ◽  
Xing-Jian Wang ◽  
Hao-Kun Du
Keyword(s):  
Quality Factor ◽  
Seismic Reflection ◽  
Seismic Attenuation ◽  
Reflection Coefficients ◽  
Variational Mode Decomposition ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
Seismic Quality Factor

Seismic attenuation as represented by the seismic quality factor [Formula: see text] has a substantial impact on seismic reflection data. To effectively eliminate the interference of reflection coefficients for [Formula: see text] estimation, a new method is proposed based on the stationary convolutional model of a seismic trace using variational mode decomposition (VMD). VMD is conducted on the logarithmic spectra extracted from the time-frequency distribution of the seismic reflection data generated from the generalized S transform. For the intrinsic mode functions after VMD, mutual information and correlation analysis are used to reconstruct the signals, which effectively eliminates the influence of the reflection coefficients. The difference between the two reconstructed logarithmic spectra within the selected frequency band produces a better linear property, and it is more suitably approximated with the linear function compared to the conventional spectral-ratio method. Least-squares fitting is finally applied for [Formula: see text] estimation. Application of this method to synthetic and real data examples demonstrates the stabilization and accuracy for [Formula: see text] estimation.

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