Value of nonstationary wavelet spectral balancing in mapping a faulted fluvial system, Bohai Gulf, China

2015 ◽  
Vol 3 (3) ◽  
pp. SS1-SS13 ◽  
Author(s):  
Huailai Zhou ◽  
Yuanjun Wang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Spectral Bandwidth ◽  
Deconvolution Method ◽  
Fluvial System ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Enhanced Resolution ◽  
Coherence Energy ◽  
Text Filtering

Seismic data with enhanced resolution allow interpreters to effectively delineate and interpret architectural components of stratigraphically thin geologic features. We used a recently developed time-frequency domain deconvolution method to spectrally balance nonstationary seismic data. The method was based on polynomial fitting of seismic wavelet magnitude spectra. The deconvolution increased the spectral bandwidth but did not amplify random noise. We compared our new spectral modeling algorithm with existing time-variant spectral-whitening and inverse [Formula: see text]-filtering algorithms using a 3D offshore survey acquired over Bohai Gulf, China. We mapped these improvements spatially using a suite of 3D volumetric coherence, energy, curvature, and frequency attributes. The resulting images displayed improved lateral resolution of channel edges and fault edges with few, if any artifacts associated with amplification of random noise.

Suppression of strong random noise in seismic data by using time-frequency peak filtering

2013 ◽  
Vol 56 (7) ◽  
pp. 1200-1208 ◽  
Author(s):  
Yue Li ◽  
BaoJun Yang ◽  
HongBo Lin ◽  
HaiTao Ma ◽  
PengFei Nie
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Time Frequency ◽  
Frequency Peak

Joint time-variant spectral analysis — Part 1: Forward modeling the effects of thin-bed layering

2018 ◽  
Vol 6 (4) ◽  
pp. T967-T983
Author(s):  
Ramses G. Meza ◽  
J. Antonio Sierra ◽  
John P. Castagna ◽  
Umberto Barbato
Keyword(s):  
Seismic Data ◽  
Seismic Isolation ◽  
Forward Modeling ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Phase Component ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Specific Frequency ◽  
Net To Gross

Using time-frequency and time-phase analysis we found that for an isolated thin bed in a binary-impedance setting, there is no observable sensitivity in preferential illumination as layered net-to-gross (NTG) changes within the isolated thin bed, regardless of the way the internal layering is distributed — either uniformly or semirandomly. The NTG signature is observed on the amplitude (magnitude) responses, rather than any specific frequency or phase component. On the other hand, external mutual thin-bed interference can significantly change the preferred phase component for each participating target. This phenomenon is largely driven by the embedded seismic wavelet that determines the nominal seismic response of an isolated thin layer and what phase component would preferentially illuminate it. For vertical separations between mutually interfering and elastically comparable thin beds in which mutual constructive interference is achieved, the target bed will be preferentially illuminated at a phase component that is very close to that of a total seismic isolation, whereas the occurrence of mutual destructive interference will cause a significant departure on the phase preferential illumination from that of an isolated seismic thin bed. All these observations can provide an avenue to yield more robust stratigraphic interpretations of seismic data and enhance the confidence on subsurface description.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

scholarly journals Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering

2016 ◽  
Vol 64 (5) ◽  
pp. 1703-1714 ◽  
Author(s):  
Pengjun Yu ◽  
Yue Li ◽  
Hongbo Lin ◽  
Ning Wu
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Time Varying ◽  
Window Length ◽  
Time Frequency ◽  
Frequency Peak

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.

CONTINUOUS VELOCITY ESTIMATION AND SEISMIC WAVELET PROCESSING

Geophysics ◽  
1972 ◽  
Vol 37 (5) ◽  
pp. 769-787 ◽  
Author(s):  
J. W. C. Sherwood ◽  
P. H. Poe
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Velocity Estimation ◽  
Velocity Data ◽  
Seismic Line ◽  
Seismic Wavelet ◽  
Common Depth Point ◽  
Long Offset ◽  
Wide Range ◽  
Record Section

An economic computer program can stack the data from several adjoining common depth points over a wide range of both dip and normal moveout. We can extract from this a set of seismic wavelets, each possessing a determined dip and normal moveout, which represent the original seismic data in an approximate and compressed form. The seismic wavelets resulting from the processing of a complete seismic line are stored for a variety of subsequent uses, such as the following: 1) Superimpose the wavelets, or a subset of them, to form a record section analogous to a conventional common‐depth‐point stacked section. This facilitates the construction of record sections consisting dominantly of either multiple or primary reflections. Other benefits can arise from improved signal‐to‐random‐noise ratio, the concurrent display of overlapping primary wavelets with widely different normal moveouts, and the elimination of the waveform stretching that occurs on the long offset traces with conventional normal moveout removal. 2) By displaying each picked wavelet as a short dip‐bar located at the correct time and spatial position and annotated with the estimated rms velocity, we can exhibit essentially continuous rms‐velocity data along each reflection. This information can be utilized for the estimation of interval and average velocities. For comparative purposes this velocity‐annotated dip‐bar display is normally formed on the same scale as the conventional common‐depth‐point stack section.

scholarly journals Denoising Method Based on Spectral Subtraction in Time-Frequency Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lei Hao ◽  
Shuai Cao ◽  
Pengfei Zhou ◽  
Lei Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Frequency Spectrum ◽  
Seismic Data ◽  
Random Noise ◽  
Seismic Exploration ◽  
Denoising Method ◽  
Time Frequency ◽  
Interpretation Errors ◽  
Effective Signal ◽  
Voice Data ◽  
Spectrum Selection

In view of the key problem that a large amount of noise in seismic data can easily induce false anomalies and interpretation errors in seismic exploration, the time-frequency spectrum subtraction (TF-SS) method is adopted into data processing to reduce random noise in seismic data. On this basis, the main frequency information of seismic data is calculated and used to optimize the filtering coefficients. According to the characteristics of effective signal duration between seismic data and voice data, the time-frequency spectrum selection method and filtering coefficient are modified. In addition, simulation tests were conducted by using different S/R, which indicates the effectiveness of the TF-SS in removing the random noise.

Sign in / Sign up

Export Citation Format

Share Document