【作者】 刘磊；

【导师】 陆文凯；

【作者基本信息】 清华大学 ， 控制科学与工程， 2008， 硕士

【摘要】 多次波问题是地震勘探领域的一个突出问题,多次波压制算法的研究已经成为地震勘探领域必不可少的一项重要内容。本文将盲信号分离算法中的约束盲分离算法与多次波压制问题相结合,对基于约束盲分离方法的多次波自适应相减技术进行了比较深入的研究,针对现有的约束盲分离方法存在的问题提出了一些改进方法并取得了一定的成果。约束盲信号分离方法是盲信号处理领域的一个研究热点,主要研究在对源信号有一定先验知识的基础上,如何将这些先验知识转化为约束条件进而利用相应的约束优化算法与盲源分离算法对盲信号分离问题进行求解。现有的用于解决多次波自适应相减问题的约束盲分离算法主要是基于地震信号的稀疏性的。本文对现有的稀疏盲分离算法提出了两种改进方法。针对现有方法中分离出的一次波的同相轴产生畸变等问题,本文引入地震信号同相轴固有的连续性作为约束条件加入到算法当中,提出了一种连续性约束独立分量分析的算法。地震信号的横向连续性可以利用预测误差滤波器来表示。在算法的实现过程中,本文对多次波压制问题建模、子波差异消除方法、混合矩阵求解方法以及欠定情况下盲分离的求解方法等多方面都进行了一定的探讨。对合成数据与真实数据的实验结果表明该方法优于现有的约束盲分离算法。对于欠定情况下的盲分离问题,文中提出了一种基于频域连续性的求解方法,利用频域预测误差滤波器转化的约束条件构建方程,将原有欠定问题的求解转化为超定方程的求解问题,对合成数据的初步实验结果表明该方法优于现有的约束盲分离算法。更多还原

【Abstract】 It has been widely acknowledged that the research of multiple attenuation algorithms has become one of the most important subjects in the area of seismic exploration. This paper focuses on the research of adaptive multiple attenuation techonology based on constrained blind source separation method. The author presents some effective methods to improve current constrained blind source separation method and thus to solve some existing issues on multiple attenuation algorithms.Constrained blind source separation method has received more and more attention in the area of blind source separation. The main focus of this subject is to convert priori knowledge on sources to constraints and solve blind source separation problem based on corresponding constrained optimization algorithms and blind source separation algorithms. Current constrained blind source separation algorithms which are designed for adaptive multiple attenuation is mainly based on sparsity of seismic data. This paper proposes two methods to improve existing constrained blind source separation algorithms.In order to address the issues such as damages to primaries etc., this paper incorporates continuity of seismic data as constraints into algorithms and thus proposes a new algorithm of continuity constrained independent component analysis. During the realization phase of this algorithm, the author researched into many methods and issues such as multiple attenuation modeling, wavelet difference elimination method, mixed matrix and under-determined blind source separation etc. Experimental results on synthetic data and real data show that the algorithm this paper presents is better than current constrained blind source separation algorithms.In addition to the research above, this paper proposes a method based on continuity in frequency domain to solve under-determined blind source separation problem. This method makes use of constraints provided by prediction error filter in frequency domain, and converts previous under-determined problem to determined problem. Experimental results on synthetic data prove that this method is more effective than current constrained blind source separation.更多还原