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.

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

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.

Study on Vibroseis Data Preprocessing Method of Metal Mining Seismic Exploration

2013 ◽  
Vol 448-453 ◽  
pp. 3751-3756 ◽  
Author(s):  
Jun Qiu Wang ◽  
Jun Lin ◽  
Xiang Bo Gong ◽  
Ran Zeng
Keyword(s):  
Seismic Data ◽  
Data Preprocessing ◽  
Seismic Exploration ◽  
Metal Mining ◽  
Seismic Section ◽  
Denoising Method ◽  
Copper Nickel ◽  
Correlation Algorithm ◽  
Preprocessing Method

In order to improve the resolution of seismic exploration, this paper mainly studied the vibroseis data preprocessing method of metal mining seismic exploration. With the characteristics of vibroseis seismic data, we studied the correlation algorithm of detecting shot gather records, system analyzed the source and classification of noise in copper-nickel detection with Hydraulic sweep source in Jinchang, and chose the denoising method according to the characteristics of noise in the shot gather records. After preprocessing, the SNR of vibroseis seismic data is effectively improved, and then the resolution of seismic section is enhanced.

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

Random noise attenuation by planar mathematical morphological filtering

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V11-V25 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Seismic Exploration ◽  
Noise Attenuation ◽  
Seismic Waveforms ◽  
Morphological Filtering ◽  
Alternative Techniques ◽  
The Difference ◽  
The Relationship

Improving the signal-to-noise ratio (S/N) of seismic data is desirable in many seismic exploration areas. The attenuation of random noise can help to improve the S/N. Geophysicists usually use the differences between signal and random noise in certain attributes, such as frequency, wavenumber, or correlation, to suppress random noise. However, in some cases, these differences are too small to be distinguished. We used the difference in planar morphological scales between signal and random noise to separate them. The planar morphological scale is the information that describes the regional shape of seismic waveforms. The attenuation of random noise is achieved by removing the energy in the smaller morphological scales. We call our method planar mathematical morphological filtering (PMMF). We analyze the relationship between the performance of PMMF and its input parameters in detail. Applications of the PMMF method to synthetic and field post/prestack seismic data demonstrate good performance compared with competing alternative techniques.

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.

Improved eigenvalue-based coherence algorithm with dip scanning

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V95-V103 ◽  
Author(s):  
Binpeng Yan ◽  
Sanyi Yuan ◽  
Shangxu Wang ◽  
Yonglin OuYang ◽  
Tieyi Wang ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Random Noise ◽  
Time Lag ◽  
Synthetic Data ◽  
Seismic Exploration ◽  
Real Field ◽  
Detection And Identification ◽  
Effective Signal ◽  
Dipping Strata ◽  
Suppress Noise

Detection and identification of subsurface anomalous structures are key objectives in seismic exploration. The coherence technique has been successfully used to identify geologic abnormalities and discontinuities, such as faults and unconformities. Based on the classic third eigenvalue-based coherence ([Formula: see text]) algorithm, we make several improvements and develop a new method to construct covariance matrix using the original and Hilbert transformed seismic traces. This new covariance matrix more readily converges to the main effective signal energy on the largest eigenvalue by decreasing all other eigenvalues. Compared with the conventional coherence algorithms, our algorithm has higher resolution and better noise immunity ability. Next, we incorporate this new eigenvalue-based algorithm with time-lag dip scanning to relieve the dip effect and highlight the discontinuities. Application on 2D synthetic data demonstrates that our coherence algorithm favorably alleviates the low-valued artifacts caused by linear and curved dipping strata and clearly reveals the discontinuities. The coherence results of 3D real field data also commendably suppress noise, eliminate the influence of large dipping strata, and highlight small hidden faults. With the advantages of higher resolution and robustness to random noise, our strategy successfully achieves the goal of detecting the distribution of discontinuities.

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