Journal of Jilin University(Earth Science Edition) ›› 2015, Vol. 45 ›› Issue (6): 1855-1861.doi: 10.13278/j.cnki.jjuese.201506303

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

Liu Xia1, Chen Chen1, Zhao Yuting2, Wang Xin1   

  1. 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

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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

CLC Number: 

  • P631.4

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