M-Estimate robust PCA for Seismic Noise Attenuation
M-Estimate robust PCA for Seismic Noise Attenuation
复制标题
DOI：
10.1109/icip.2016.7532679
复制
发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Hojjat Akhondi Asl;J. Nelson
中科院分区：
文献类型：
--
作者：
Hojjat Akhondi Asl;J. Nelson
鲁棒主成分分析（PCA）方法在处理数据中的异常值时在地震环境噪声衰减方面表现出非常有希望的结果。然而，该模型假设噪声为一般高斯分布加上稀疏离群值。然而，在地震数据中，噪声标准变化可能因地而异，导致更重尾的噪声分布。在本文中，我们提出了一种新的方法，解决了凸极小化问题的鲁棒PCA方法与M-估计罚函数。实验结果表明，该方法的性能优于鲁棒PCA方法。
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.