Seismic data usually contains random noise generated from a wide variety of unpredictable irregular factors. Independent Component Analysis (ICA) is a new multi-dimensional signal processing method based on high level statistics able to achieve separation of source signals in the absence of priori information. This article attempts to apply ICA to removing random noise from seismic exploration data and analyzes the assumptions and inherent uncertainties of the technique. We use an improved preprocessing algorithm to remove additive white Gaussian noise (AWGN) first, and then, by joint approximate diagonalization of eigen-matrices (JADE algorithm), further process the data to blindly separate the effective signal from the non-Gaussian distributed random noise. Furthermore, the article establishes the normative similarity coefficient criteria to identify effective signal to resolve the order uncertainty problem in ICA and achieve an effective signal extraction. Simulation experiments and the actual seismic data processing experiments show that the algorithm proposed in. this paper effectively removed the random noise.%独立分量分析是近年由盲信号理论发展起来的一种新的多维信号处理方法,在没有先验信息的情况下,能够实现源信号的分离.地震资料中常常包含随机噪声,它是由各种不可预知因素综合作用而成,无统一规律.文中将ICA应用于去除随机噪声问题,在对地震数据特点进行分析的基础上,建立了随机噪声盲分离的ICA模型,并对其假设条件和固有不确定问题进行了深入分析,利用文中改进的稳健预处理算法,先去除加性高斯白噪声,然后将预处理后的数据采用JADE盲分离算法分离出有效信号和非高斯分布的随机噪声,并建立能够辨识有效信号的准则,解决了ICA分离后次序不确定问题,实现了有效信号的提取.仿真实验和对实际地震资料处理表明文中提出的算法能够有效地去除随机噪声.
展开▼
