Varying-Window-Length Time-Frequency Peak Filtering and its Application to Seismic Data

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.

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An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

CONVERTER ◽

10.17762/converter.304 ◽

2021 ◽

pp. 407-418

Author(s):

Jie Wu, Xiaojuan Chen, Zhaohua Zhang

Keyword(s):

White Noise ◽

Thermal Noise ◽

Low Frequency ◽

Window Length ◽

Time Frequency ◽

Noise Characteristics ◽

Small Window ◽

Frequency Peak ◽

Variable Window ◽

Conventional Algorithm

The generation of 1/f noise is closely related to the quality defects of IGBT devices. In the process of detecting IGBT single tube noise, thermal noise and shot noise show obvious white noise characteristics in the low frequency band, which are detected under the background of strong white noise 1/f noise can characterize the performance of IGBT devices. Therefore, on the basis of the Time-Frequency Peak Filtering (TFPF) algorithm, a two-dimensional time-domain adaptive T-ATFPF algorithm is proposed, and the adaptive segmentation is realized by means of the confidence interval crossing criterion based on Chebyshev’s inequality. Variable window length,use a small window length to process the signal section, which retains more detailed information of the effective signal.Use a larger window length to process the buffer section to ensure a smooth transition.Use the large window length to process the noise section, which more effectively suppresses randomness for noise, apply T-ATFPF to artificial synthesis model and actual model. Experimental results indicate that compared with the conventional algorithm, the improved method can better recover 1/f noise, and the ratio of signal to noise is greatly improved by about 1.3dB.

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Random Noise Suppression from Seismic Data Using Time-frequency Peak Filtering

71st EAGE Conference and Exhibition incorporating SPE EUROPEC 2009 ◽

10.3997/2214-4609.201400214 ◽

2009 ◽

Cited By ~ 3

Author(s):

A. Roshandel Kahoo ◽

H. R. Siahkoohi

Keyword(s):

Seismic Data ◽

Noise Suppression ◽

Random Noise ◽

Time Frequency ◽

Frequency Peak

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Constrained least-squares spectral analysis: Application to seismic data

Geophysics ◽

10.1190/geo2011-0210.1 ◽

2012 ◽

Vol 77 (5) ◽

pp. V143-V167 ◽

Cited By ~ 53

Author(s):

Charles I. Puryear ◽

Oleg N. Portniaguine ◽

Carlos M. Cobos ◽

John P. Castagna

Keyword(s):

Spectral Analysis ◽

Fourier Transform ◽

Wavelet Transform ◽

Seismic Data ◽

Continuous Wavelet ◽

Window Length ◽

Short Time Fourier Transform ◽

Constrained Least Squares ◽

Time Frequency ◽

Short Time

An inversion-based algorithm for computing the time-frequency analysis of reflection seismograms using constrained least-squares spectral analysis is formulated and applied to modeled seismic waveforms and real seismic data. The Fourier series coefficients are computed as a function of time directly by inverting a basis of truncated sinusoidal kernels for a moving time window. The method resulted in spectra that have reduced window smearing for a given window length relative to the discrete Fourier transform irrespective of window shape, and a time-frequency analysis with a combination of time and frequency resolution that is superior to the short time Fourier transform and the continuous wavelet transform. The reduction in spectral smoothing enables better determination of the spectral characteristics of interfering reflections within a short window. The degree of resolution improvement relative to the short time Fourier transform increases as window length decreases. As compared with the continuous wavelet transform, the method has greatly improved temporal resolution, particularly at low frequencies.

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