摘要
由于地震数据中存在各种各样的噪声,怎样去压制或者滤除这些噪声,以至提高信噪比成为当前地震信号处理中关键步骤.本文根据地震数据中随机噪声干扰和可能存在的工业单频噪声干扰的特点,采用主成分分析技术,即通过正交分解得到数据矩阵的主分量,并利用不同的主分量进行信号重构,从而达到去除单频干扰和随机噪声的目的,有效地提高了地震数据的信噪比.
How to denoise or filter interference has become a key part of seismic signal processing for improving the signal to noise ratio because there exist different kinds of noise in seismic data. In this paper, principal component analysis (PCA) is applied to denoise according to the features of random noise and single frequency interference from industry. This method proposed can be divided into two steps. First, principal component is obtained by single value decomposition of seismic data matrix. Second, the signal reconstruction is gained using different principal components. The experimental results show that the method presented can obtain higher signal to noise ratio owing to the suppression of single frequency interference and random noise.
出处
《地球物理学进展》
CSCD
北大核心
2011年第3期1039-1044,共6页
Progress in Geophysics
基金
国家863高技术研究发展计划项目(2006AA09A102-12)资助
关键词
信噪比
随机噪声
单频干扰
主分量分析
特征向量
signal to noise ratio, randome noise, single frequency interference, principal component analysis, eigenvector