An Events Rearrangement Strategy-Based Robust Principle Component Analysis

Random noise in seismic data can affect the performance of reservoir characterization and interpretation, which makes denoising become an essential procedure. This letter focuses on suppressing random noise in poststack seismic data while preserving the edges of desired signals. Due to the lateral continuity of seismic data, polynomial fitting (PF) method can be a good alternative in attenuating random noise. However, discontinuities exist widely in poststack seismic data, which might be damaged by the PF filter. By contrast, principle component analysis (PCA)-based filters have better performance in edge preserving, but there appear artifacts in the denoised results using the PCA-based filters. Thus, we propose an edge-preserving polynomial PCA filter which combines advantages of the PF and PCA methods by optimizing a PCA problem with a weighted polynomial constraint. The weight coefficient is determined adaptively according to the signal-to-noise ratio estimation and the energy proportion in the selected analysis window, which can help distinguish the horizontal continuous events and the edges effectively. To deal with the complicated slopes which make the local linear hypothesis invalid, we introduce a robust local slope estimation method and apply the slope estimation-based event tracing strategy to horizontally align the data set. Synthetic and field data examples show that the proposed method has a better performance in noise attenuation and edge preserving, compared with the edge-preserving PF method. In addition, the denoised results are free from artifacts.