独立分量分析 (ICA)通过对非高斯分布数据进行有效表示,获得在统计学上独立的各个分量。这种表示可以获取数据的基本结构,包括特征提取和信号分离。简述ICA基本理论和快速算法,对照主分量分析 (PCA)的Karhunen Loeve(K L)变换,提出独立分量变换 (ICT)概念。在分析地震信号特点的基础上,对模拟和实际含噪地震道进行独立分量变换、信噪分离和去噪处理初步探索,重建获得令人满意的去噪结果。研究表明,ICA在勘探地震信号处理中具有应用前景.

ICA is a novel stat istical method developed recently, which is used to find a representat ion of the Non-Gaussian mult ivariate data. The representation shows that each component of the computed vector is independent stat istically, or as independent as possible. In application, this kind of transformat ion aims to capture the basic structures of the analyzed data, including features abst raction and separat ion of signals. Presents the fundamental theory and fast algorithms, at the same time, implement the Fast ICA and itsupdated version. Compared w ith PCA or K-L t ransformat ion, proposes the concept of Independent component transformat ion ( ICT). On the basis of analyzing the features of seismic signals, does preliminary studies and try to apply ICA on seismic sig nal processing. Research results show the good perspect ive of ICA application to seismic signal processing.