Sinusoidal Seismic Noise Suppression Using Randomized Principal Component Analysis

Sinusoidal interference is a common type of noise caused by power facilities. It usually has the fixed frequency and large amplitude which will have a negative impact on seismic data processing. We propose a new approach for suppressing sinusoidal noise, which is called randomized principal component analysis. It takes advantage of the characteristics of noisy record itself and does not need to calculate the amplitude or phase of the sinusoidal noise. The key algorithm steps of this method can be illustrated as follows: first, interpolation will be applied to get the improved time accuracy. Second, frequency search and narrow-band-pass filter are used to separate the noise-dominated component and determine its precise period. Then, for one of the periods, we use several periods around it to form a “section”. The randomization is then implemented along the lateral direction to disturb the reflected signal in this “section”. Besides, principal component analysis is applied to extract sinusoidal noise which still has strong correlation, and to filter the signal which has been converted into random noise. Last, the separated noise is subtracted from the record. Via the synthetic model and field examples, this method is compared to the notch filter to demonstrate its superiority.

[1]  James E. Gaiser,et al.  Adaptive Rejection of High-LineContamination , 1983 .

[2]  Jonathan Li,et al.  SAR Image Denoising via Clustering-Based Principal Component Analysis , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Yangkang Chen,et al.  Seismic Noise Attenuation Using Unsupervised Sparse Feature Learning , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Yanchun Liang,et al.  A Fuzzy-Statistics-Based Principal Component Analysis (FS-PCA) Method for Multispectral Image Enhancement and Display , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Guochang Liu,et al.  Random noise attenuation using f-x regularized nonstationary autoregression , 2012 .

[6]  Wail A. Mousa,et al.  An Efficient Undersampled High-Resolution Radon Transform for Exploration Seismic Data Processing , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Antoine Saucier,et al.  A fast and accurate frequency estimation method for canceling harmonic noise in geophysical records , 2006 .

[8]  Yangkang Chen,et al.  Damped multichannel singular spectrum analysis for 3D random noise attenuation , 2015, GEOPHYSICS.

[9]  Mauricio D. Sacchi,et al.  Widely linear denoising of multicomponent seismic data , 2019, Geophysical Prospecting.

[10]  L. Canales.  Random Noise Reduction , 1984 .

[11]  Karl E. Butler,et al.  Subtraction of powerline harmonics from geophysical records , 1993 .

[12]  Elena Shoshitaishvili,et al.  Data Improvement By Subtraction of High-amplitude Harmonics From the 2D Land Vertical- And Multi-component Seismic Data Acquired Over the Cheyenne Belt In SE Wyoming. , 2001 .

[13]  Yangkang Chen,et al.  Simultaneous Denoising and Interpolation of 3-D Seismic Data via Damped Data-Driven Optimal Singular Value Shrinkage , 2017, IEEE Geoscience and Remote Sensing Letters.

[14]  Yangkang Chen,et al.  Random noise attenuation using local signal-and-noise orthogonalization , 2015 .

[15]  A. Frank Linville,et al.  CANCELING STATIONARY SINUSOIDAL NOISE , 1992 .

[16]  Guangyi Chen,et al.  Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[17]  F. Symonds,et al.  Characteristics of the Eastern Interconnection Line Frequency , 1982, IEEE Transactions on Power Apparatus and Systems.

[18]  Derman Dondurur,et al.  A mean-based filter to remove power line harmonic noise from seismic reflection data , 2018 .

[19]  Karl E. Butler,et al.  Cancellation of multiple harmonic noise series in geophysical records , 2003 .

[20]  Mrinal K. Sen,et al.  3D seismic data de-noising and reconstruction using Multichannel Time Slice Singular Spectrum Analysis , 2017 .

[21]  Mike Galbraith.  Random Noise Attenuation By F-X Prediction: A Tutorial , 1991 .

[22]  Stephen K. Chiu,et al.  Coherent and random noise attenuation via multichannel singular spectrum analysis in the randomized domain , 2013 .

[23]  Leiv-J. Gelius,et al.  Attenuation of marine seismic interference noise employing a customized U‐Net , 2020, Geophysical Prospecting.

[24]  Feng Wang,et al.  Residual Learning of Deep Convolutional Neural Network for Seismic Random Noise Attenuation , 2019, IEEE Geoscience and Remote Sensing Letters.

[25]  Jianwei Ma,et al.  A compound method for random noise attenuation , 2018, Geophysical Prospecting.

[26]  Jianghai Xia,et al.  Design of a Hum Filter for Suppressing Power-line Noise in Seismic Data , 2000 .

[27]  Hui Zhou,et al.  Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.