M-Estimate robust PCA for Seismic Noise Attenuation

Abstract

The robust principal component analysis (PCA) method has shown very promising results in seismic ambient noise attenuation when dealing with outliers in the data. However, the model assumes a general Gaussian distribution plus sparse outliers for the noise. In seismic data however, the noise standard variation could vary from one place to another leading to a more heavy-tailed noise distribution. In this paper, we present a new method which solves a convex minimisation problem of the robust PCA method with an M-estimate penalty function. Our empirical results show that the proposed method can outperform the robust PCA method.

Auto-REP: An Automated Regression Pipeline Approach for High-efficiency Earthquake Prediction Using LANL Data

“…Traditional methods usually require manual frequency domain analysis of the waveform. Denoising through Robust Principal Component Analysis (R-PCA), as described in [12], [13], and dictionary learning [14] are two additional waveform approaches. Ross et al [15] presented PhaseLink, a system for grid-free earthquake phase [18] proposed a spatialtemporal data analysis-based earthquake prediction study.…”