Abstract:

Improving the signal-to-noise ratio of seismic data is an important goals of seismic data processing. Although conventional seismic data de-noising methods can effectively suppress random noise, they are less effective in suppressing outlier or erratic noise with non-Gaussian distribution. In this paper, we present a robust principal component analysis (RPCA) method to suppress erratic noise in seismic data. This method yields ideal noise-free data by implementing robust low-rank approximation to seismic data in the frequency-space domain. For the constructing objective function, the nuclear norm minimization model is used to obtain ideal low-rank approximation data, and the l₁ norm minimization model is adopted to estimate the outlier noise. Furthermore, the inversion problem is solved with the augmented Lagrange multiplier method. De-noising results of the synthetic data and real data verify the effectiveness of this method. Meanwhile comparison with de-noising results of the conventional F-XY domain prediction filtering method also demonstrates that the proposed method can both effectively suppress erratic noise and protect the effective wave energy properly.

Key words: robust principal component analysis, erratic noise, matrix rank reduction