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.

Abstract licence: CC BY-NC-ND

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