基于粒子群快速优化MP算法的多子波分解与重构

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。

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

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 .