Seismic Data Resolution Improvement by Compensating Time-frequency Spectrum of Synchrosqueezing Wavelet Transform

2017 ◽  
Author(s):  
Yan Zhang* ◽  
Zhenchun Li ◽  
Jiao Wang
Keyword(s):  
Wavelet Transform ◽  
Frequency Spectrum ◽  
Seismic Data ◽  
Time Frequency ◽  
Data Resolution ◽  
Resolution Improvement

Wavelet transform with generalized beta wavelets for seismic time-frequency analysis

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. O47-O56 ◽  
Author(s):  
Zhiguo Wang ◽  
Bing Zhang ◽  
Jinghuai Gao ◽  
Qingzhen Wang ◽  
Qing Huo Liu
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Significant Information ◽  
Trade Off ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Reflection Seismic ◽  
Seismic Waveform ◽  
Frequency Representation

Using the continuous wavelet transform (CWT), the time-frequency analysis of reflection seismic data can provide significant information to delineate subsurface reservoirs. However, CWT is limited by the Heisenberg uncertainty principle, with a trade-off between time and frequency localizations. Meanwhile, the mother wavelet should be adapted to the real seismic waveform. Therefore, for a reflection seismic signal, we have developed a progressive wavelet family that is referred to as generalized beta wavelets (GBWs). By varying two parameters controlling the wavelet shapes, the time-frequency representation of GBWs can be given sufficient flexibility while remaining exactly analytic. To achieve an adaptive trade-off between time-frequency localizations, an optimization workflow is designed to estimate suitable parameters of GBWs in the time-frequency analysis of seismic data. For noise-free and noisy synthetic signals from a depositional cycle model, the results of spectral component using CWT with GBWs display its flexibility and robustness in the adaptive time-frequency representation. Finally, we have applied CWT with GBWs on 3D seismic data to show its potential to discriminate stacked fluvial channels in the vertical sections and to delineate more distinct fluvial channels in the horizontal slices. CWT with GBWs provides a potential technique to improve the resolution of exploration seismic interpretation.

Instantaneous spectral attributes using scales in continuous-wavelet transform

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. WA137-WA142 ◽  
Author(s):  
Satish Sinha ◽  
Partha Routh ◽  
Phil Anno
Keyword(s):  
Wavelet Transform ◽  
Frequency Spectrum ◽  
Continuous Wavelet Transform ◽  
Time Window ◽  
Frequency Resolution ◽  
Rock Properties ◽  
Center Frequency ◽  
Continuous Wavelet ◽  
Frequency Spectra ◽  
Time Frequency

Instantaneous spectral properties of seismic data — center frequency, root-mean-square frequency, bandwidth — often are extracted from time-frequency spectra to describe frequency-dependent rock properties. These attributes are derived using definitions from probability theory. A time-frequency spectrum can be obtained from approaches such as short-time Fourier transform (STFT) or time-frequency continuous-wavelet transform (TFCWT). TFCWT does not require preselecting a time window, which is essential in STFT. The TFCWT method converts a scalogram (i.e., time-scale map) obtained from the continuous-wavelet transform (CWT) into a time-frequency map. However, our method includes mathematical formulas that compute the instantaneous spectral attributes from the scalogram (similar to those computed from the TFCWT), avoiding conversion into a time-frequency spectrum. Computation does not require a predefined window length because it is based on the CWT. This technique optimally decomposes a multiscale signal. For nonstationary signal analysis, spectral decomposition from [Formula: see text] has better time-frequency resolution than STFT, so the instantaneous spectral attributes from CWT are expected to be better than those from STFT.

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.

Improved Hilbert-Huang Transform and its Application in Seismic Data Spectrum Decomposition

2012 ◽  
Vol 152-154 ◽  
pp. 920-923
Author(s):  
Ping Ping Bing ◽  
Si Yuan Cao ◽  
Jiao Tong Lu
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Narrow Band ◽  
Numerical Study ◽  
Wavelet Packet ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Spectrum Decomposition

In the conventional seismic data time-frequency analysis, the wavelet transform, wigner ville distribution and so on, cannot meet the high precision time-frequency analysis requirements because of uncertainty principle and cross-term interference. The recently popular Hilbert-Huang transform (HHT) although overcomes these conventional methods’ deficiencies; it still has some unsolved deficiencies due to the theory imperfect. This paper focuses on an improved HHT so as to ameliorate the defect of original HHT. First of all, the wavelet packet transform (WPT) as the preprocessing will be used to the inspected signal, to get some narrow band signals. Then use the empirical mode decomposition (EMD) on the narrow band signals and get the real intrinsic mode function (IMF) by the method of correlation coefficient. From the numerical study and comparison of improved HHT, wavelet transform and HHT, it proves the validity and advantages of this improved method. At last, the improved HHT is applied to marine seismic data by the spectrum decomposition technology, and it well reveals the low frequency shadow phenomenon of the reservoir. The results show that this new method has effectiveness and feasibility in seismic data spectrum decomposition.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

Time-frequency analysis of seismic data using synchrosqueezing wavelet transform

2014 ◽  
Author(s):  
Yangkang Chen* ◽  
Tingting Liu ◽  
Xiaohong Chen ◽  
Jingye Li ◽  
Erying Wang
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Time Frequency Analysis ◽  
Time Frequency
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