基于匹配追踪算法的地震多子波分解与重构技术研究

发布时间：2018-03-20 02:26

本文选题：多子波　切入点：匹配追踪　出处：《东北石油大学》2015年硕士论文　论文类型：学位论文

【摘要】：地震信号是由不同能量、不同形状的地震子波叠加得到的复合谐波。采集地震信号中将能够描述地质构造和岩性信息的有效信号分离出来是直接决定地震资料解释正确性的关键一步。目前常用的传统地震模型由于其地震子波形式单一,并不能很好的满足实际地震信号处理的需要,而多子波地震道模型因其子波形式丰富、能很好地模拟地震波传播过程的特点更能满足实际应用。多子波分解与重构技术可以对信号进行分解,按照其频率、振幅、能量等属性的特点分解成一系列子波序列的集合,再把得到的子波按照一定规则重新构建。多子波分解与重构技术的研究有着重要的实际意义和应用价值。本文主要研究了基于匹配追踪算法的地震多子波分解与重构技术。本文首先介绍了多子波地震道模型与多子波分解与重构的基本思想,然后介绍了信号稀疏表示的基本原理,主要介绍了匹配追踪算法的原理和实现步骤。匹配追踪算法能够根据地震信号自身的特点进行自适应分解,但是它庞大的计算量却是一项难以避免的缺点。针对这个问题,本文提出了一种基于改进粒子群优化的匹配追踪算法,用于快速搜索地震信号稀疏分解的最优匹配原子,改进内容分为两个方面:一是在粒子群算法中引入一种多项式变异算子,有效地避免了搜索最优解的过度集中;二是在迭代过程中,将参数搜索区域确定在高斯函数能量集中的部分,避免了搜索过程的“贪婪性”,能有效降低稀疏分解复杂度。采用Gabor子波构造原子字典,对实际地震信号进行多子波分解与重构,效果提升显著。Ricker子波作为一种常用的地震子波,与地震信号的相关程度比Gabor子波要高。选用Ricker子波构造时频原子字典,应用与Gabor子波相类似的四参数编码形式,采用了基于Ricker子波时频原子字典的多子波分解与重构算法。在实验环节,对实际地震信号进行多子波分解与重构,通过实验结果证明,这两种算法均在保持匹配追踪的分解精度基础上,使计算效率得到大幅提高,基于Ricker子波时频原子字典的算法效果更加突出。
[Abstract]:The seismic signal is different from the energy, harmonic superposition composite seismic wavelet with different shapes are obtained. The effective signal acquisition of seismic signal will be able to describe the geological structure and lithology information is separated directly determines the interpretation of seismic data is a key step to the accuracy of the commonly used traditional seismic model because of the seismic wavelet is not a single form, good to meet the actual needs of seismic signal processing, and multi wavelet seismic trace model because of its rich form of wavelet, can well simulate the characteristics of seismic wave propagation. It can better meet the actual application of multi wavelet decomposition and reconstruction technique can decompose the signal according to the frequency, amplitude, energy, etc. into a set of wavelet series, the wavelet according to certain rules to re construct. Study on multi wavelet decomposition and reconstruction technology has important practical significance And the application value. This paper mainly studies the decomposition based on matching pursuit algorithm of seismic wavelet and reconstruction technology. This paper introduces the multi wavelet seismic trace model with multi wavelet decomposition and reconstruction of the basic thought, basic principle and then introduces the signal sparse representation, mainly introduces the matching pursuit algorithm principle and implementation steps of matching pursuit. The algorithm can adaptively decompose seismic signal according to the characteristics of its own, but it is a huge amount of calculation to avoid the disadvantages. To solve this problem, this paper proposes an improved particle swarm optimization matching tracking algorithm based on the fast search for optimal seismic signal sparse decomposition matching atom, improved content is divided into two aspects: one is to introduce a polynomial mutation operator in particle swarm algorithm, effectively avoid the excessive concentration of searching optimal solution in the two iteration; In the process, the parameters of the search area identified in the Gauss function of energy concentration, to avoid the search process of "greed", can effectively reduce the complexity of sparse decomposition. Using Gabor wavelet dictionary, the seismic signal of multi wavelet decomposition and reconstruction, the effect of lifting.Ricker wavelet was applied as a kind of earthquake wavelet, and related degree of seismic signal than Gabor wavelet to choose Ricker high. The construction of wavelet time-frequency dictionary, four parameter encoding application form with Gabor wavelet similar, using multi wavelet decomposition of Ricker wavelet time-frequency dictionary and reconstruction algorithm based on. In the experiment, the actual seismic signal of multi wavelet decomposition with the reconstruction, the experimental results show that these two algorithms are in matching pursuit decomposition accuracy on the basis of the computation efficiency is greatly improved, the wavelet time-frequency atom based on Ricker The algorithm of the dictionary is more effective.

【学位授予单位】：东北石油大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P631.4

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