基于粒子群快速优化MP算法的多子波分解与重构

doi:10.13278/j.cnki.jjuese.201506303

吉林大学学报(地球科学版) ›› 2015, Vol. 45 ›› Issue (6): 1855-1861.doi: 10.13278/j.cnki.jjuese.201506303

基于粒子群快速优化MP算法的多子波分解与重构

刘霞¹, 陈晨¹, 赵玉婷², 汪鑫¹

1. 东北石油大学电气信息工程学院, 黑龙江大庆 163318;
2. 中国石油天然气集团公司大庆油田有限责任公司, 黑龙江大庆 163002

收稿日期:2015-01-05 发布日期:2015-11-26
作者简介:刘霞(1970),女,教授,博士,主要从事信号处理、鲁棒滤波、模型降阶、网络控制系统等研究,E-mail:liu-xia2k@163.com。
基金资助:
黑龙江省自然科学基金项目(F201404)

Multi-Wavelet Decomposition and Reconstruction Based on Matching Pursuit Algorithm Fast Optimized by Particle Swarm

Liu Xia¹, Chen Chen¹, Zhao Yuting², Wang Xin¹

1. School of Electrical Engineering and Information, Northeast Petroleum University, Daqing 163318, Heilongjiang, China;
2. Daqing Oilfield, China National Petroleum Corporation, Daqing 163002, Heilongjiang, China

Received:2015-01-05 Published:2015-11-26

摘要/Abstract

摘要：

针对地震信号多子波分解与重构技术中匹配追踪算法能够根据地震信号自身特点进行自适应分解、但其计算量庞大的问题,笔者提出一种粒子群快速优化算法,用于快速搜索地震信号稀疏分解的最优匹配原子。即在迭代过程中,将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。同时,在粒子群算法中引入了一种多项式变异算子,可以有效避免搜索最优解的过度集中。实验结果证明,此算法将匹配追踪的分解精度提高了67倍,更使计算效率提高了153倍。

关键词: 多子波, 匹配追踪, 粒子群

Abstract:

In a multi-wavelet decomposition and reconstruction of seismic signal, the matching pursuit algorithm can be adaptive according to the characteristics of the seismic signal itself. In view of the large amount of calculation, the author presents a particle swarm fast optimization algorithm, which is used for fast search optimum matching atoms of seismic signal sparse decomposition. In concrete, the searching area is determined by the energy concentrated part of Gaussian function in the process of iteration. This can avoid the greediness during the searching process, and effectively reduce the sparse decomposition complexity. At the same time, a polynomial mutation operator is introduced in the particle swarm optimization algorithm, which can effectively avoid the excessive concentration during searching the optimal solution. The experimental results show that the algorithm can reach a precision of matching pursuit decomposition 67 times higher than before, and increase the calculation efficiency by 153 times.

Key words: multi-wavelet, matching pursuit, particle swarm optimization

中图分类号:

P631.4

刘霞, 陈晨, 赵玉婷, 汪鑫. 基于粒子群快速优化MP算法的多子波分解与重构[J]. 吉林大学学报(地球科学版), 2015, 45(6): 1855-1861.

Liu Xia, Chen Chen, Zhao Yuting, Wang Xin. Multi-Wavelet Decomposition and Reconstruction Based on Matching Pursuit Algorithm Fast Optimized by Particle Swarm[J]. Journal of Jilin University(Earth Science Edition), 2015, 45(6): 1855-1861.

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基于粒子群快速优化MP算法的多子波分解与重构

Multi-Wavelet Decomposition and Reconstruction Based on Matching Pursuit Algorithm Fast Optimized by Particle Swarm

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