利用独立分量分析法去除地震噪声

 独立分量分析（ICA）作为盲源分离（BSS）的一种新方法,是分解观测数据中独立信息的有力工具。以往的ICA算法一般假设噪声可以忽略不计，而实际的观测数据中又常常包含一些加性噪声。对于加性噪声的影响不能忽略的情况下，改进的ICA算法首先利用非零时间滞后协方差，应用两步特征值分解法(EVD)可成功地去除部分加性噪声的影响；再利用ICA算法就能更好地分离出原信号。本文通过对地震理论模型和实际资料的试验，说明改进的ICA算法能够有效地克服加性噪声对常规ICA算法的影响，能够分离出地震资料中的有效信号，从而实现利用独立分量分析压制地震资料噪声的目的。

Using independent component analysis to eliminate seismic noises

As a new approach of blind sources separation (BSS), the independent component analysis (ICA) is powerful tool of decomposing independent information in surveyed data. Former ICA algorithm generally supposes that the noises can be neglected, but practically, the additional noises are often included in surveyed data. Under the condition that the additional noises can not be neglected, the improved ICA algorithm first uses non-zero time-lapse covariance and two-way eigenvalue decomposition (EVD) to successfully eliminate the influence of part of additional noises;then the original signals can be better separated by using the ICA algorithm again.The paper demonstrated by the tests of seismic theoretical model and practical data that improved ICA algorithm can effectively overcome the influence of additional noises on ordinary ICA algorithm and separate the useful signals in seismic data,realizing the goal by using independent component analysis to suppress the noises in seismic data.