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.

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.

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

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.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

Q estimation and its application in the 3D shallow weathering zone

2017 ◽  
Vol 5 (1) ◽  
pp. SC29-SC38 ◽  
Author(s):  
Ying Liu ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Zhikai Wang ◽  
Yiran Xu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Empirical Formula ◽  
Synthetic Data ◽  
Spectral Ratio ◽  
Frequency Resolution ◽  
Deep Part ◽  
Time Frequency ◽  
Generalized S Transform ◽  
Velocity Information ◽  
Weathering Zone

Attenuation in the shallow weathering zone is relatively strong, causing severe energy loss during wave propagation. It is difficult to estimate accurate [Formula: see text] values in the shallow weathering zone, and the influence of shallow weathering zone is seldom considered into attenuation estimation and compensation in the deep part. We achieved [Formula: see text] value estimation where there exist microlog data in the shallow weathering zone using the generalized S transform (GST); then, we establish an empirical formula using the velocity and [Formula: see text] value estimated with microlog data; finally, the [Formula: see text] value in the 3D shallow weathering zone can be obtained using the established formula and the velocity information. During the first procedure, the GST is used to provide reasonable time-frequency resolution, and linear regression is used in the obtained logarithmic spectral ratio to get the estimated [Formula: see text] value. An empirical formula is established using the estimated [Formula: see text] value and the velocity where there exists microlog data in the second procedure. In the third step, [Formula: see text] estimation in the whole shallow weathering zone can be obtained using the established formula and the velocity information, which can overcome the inaccuracy of spatial interpolation with the estimated [Formula: see text] factors where there exist different twin-well microlog data. Attenuation compensation to seismic data obtained from the deep part is carried out to prove the effectiveness of the estimated [Formula: see text] in the shallow weathering zone. After compensation, the resolution of seismic data is effectively increased, which demonstrates the validity of the estimated [Formula: see text] values in the shallow weathering zone. Synthetic data and field data examples demonstrate the validity of our method.

scholarly journals Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5025
Author(s):  
Xuegong Zhao ◽  
Hao Wu ◽  
Xinyan Li ◽  
Zhenming Peng ◽  
Yalin Li
Keyword(s):  
High Resolution ◽  
Reflection Coefficient ◽  
Objective Function ◽  
Frequency Domain ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Frequency Resolution ◽  
Basis Pursuit ◽  
Time Frequency

Seismic reflection coefficient inversion in the joint time-frequency domain is a method for inverting reflection coefficients using time domain and frequency domain information simultaneously. It can effectively improve the time-frequency resolution of seismic data. However, existing research lacks an analysis of the factors that affect the resolution of inversion results. In this paper, we analyze the influence of parameters, such as the length of the time window, the size of the sliding step, the dominant frequency band, and the regularization factor of the objective function on inversion results. The SPGL1 algorithm for basis pursuit denoising was used to solve our proposed objective function. The applied geological model and experimental field results show that our method can obtain a high-resolution seismic reflection coefficient section, thus providing a potential avenue for high-resolution seismic data processing and seismic inversion, especially for thin reservoir inversion and prediction.

Amplitude spectrum compensation and phase spectrum correction of seismic data based on the generalized S transform

2014 ◽  
Vol 11 (4) ◽  
pp. 468-478 ◽  
Author(s):  
Huai-Lai Zhou ◽  
Jun Wang ◽  
Ming-Chun Wang ◽  
Ming-Cheng Shen ◽  
Xin-Kun Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Amplitude Spectrum ◽  
Phase Spectrum ◽  
S Transform ◽  
Generalized S Transform ◽  
Spectrum Correction

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

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