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

Time-frequency peak filtering (TFPF) is highly efficient in suppressing random noise in seismic data. Although the hypothesis of stationary Gaussian white noise cannot be fulfilled in practical seismic data, TFPF can effectively suppress white and colored random noise with different intensities, as can be theoretically demonstrated by detecting such noise in synthetic seismic data. However, a “zero-drift” effect is observed in the filtered signal and is independent of the average power and variance of the random noise, but related to its mean value. Furthermore, we consider the situation where the local linearization of the seismic data cannot be satisfied absolutely and study the “distortion” characteristics of the filtered signal using TFPF on a triangular wave. We found that over-compensation is possible in the frequency band for the triangular wave. In addition, it is nonsymmetrical and has a relationship to the time-varying curvature of the seismic wavelet. The results also present an improved approach for TFPF.

[1]  Jerry M. Harris,et al.  Multi-component wavefield simulation in viscous extensively dilatancy anisotropic media , 1999 .

[2]  Shi-Qiang Yuan,et al.  The solutions of equation-based noise detector for an adaptive median filter , 2006, Pattern Recognit..

[3]  Thomas Kailath,et al.  Modern signal processing , 1985 .

[4]  Michael Jervis,et al.  Edge Preserving Filtering On 3-D Seismic Data Using Complex Wavelet Transforms , 2006 .

[5]  Felix J. Herrmann,et al.  Curvelet-based seismic data processing : A multiscale and nonlinear approach , 2008 .

[6]  Yang Liu,et al.  A 2D multistage median filter to reduce random seismic noise , 2006 .

[7]  G. Duncan,et al.  Median filter behaviour with seismic data1 , 1995 .

[8]  Mostefa Mesbah,et al.  Signal enhancement by time-frequency peak filtering , 2004, IEEE Transactions on Signal Processing.

[9]  Complex Polarization Analysis Based on Windowed Hilbert Transform and Its Application , 2005 .

[10]  Hongbo Lin,et al.  Recovery of Seismic Events by Time-Frequency Peak Filtering , 2007, 2007 IEEE International Conference on Image Processing.

[11]  Dinghui Yang,et al.  Noise reduction and drift removal using least-squares support vector regression with the implicit bias term , 2010 .

[12]  M. Baan,et al.  Local singular value decomposition for signal enhancement of seismic data , 2007 .

[13]  Wenkai Lu,et al.  Local linear coherent noise attenuation based on local polynomial approximation , 2006 .

[14]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[15]  J. Schön,et al.  SEG Technical Program Expanded Abstracts 2013 , 2013 .

[16]  Wenkai Lu,et al.  Adaptive noise attenuation of seismic images based on singular value decomposition and texture direction detection , 2006 .

[17]  Deng Xiao.  Performance analysis of least squares support vector regression filtering system , 2010 .

[18]  Hongbo Lin,et al.  Ability to detect weak effective seismic signals by utilizing chaotic vibrator system , 2006 .

[19]  T. Ulrych,et al.  Physical Wavelet Frame Denoising , 2003 .