Seismic wavelet extraction based on adaptive chaotic embedded particle swarm optimization algorithm
Dai Yongshou1, Wei Yuqin2, Zhang Yanan1, Chen Jian1, Ding Jingjie1
1. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China;
2. Beris Engineering and Research Corporation, Qingdao, Shandong 266555, China
Abstract：An adaptive chaotic embedded particle swarm optimization algorithm (ACEPSO) based on Tent map has been proposed in this paper, and it has been used in cumulant matching and solving objective function to get the wavelet parameters. ACEPSO embeds chaotic variables in standard particle swarm optimization algorithm, and adjusts the inertia weight and learning factors nonlinearly and adaptively. It also estimates particles whether being focusing or discrete by judging the population fitness variance of particle swarm and average distance amongst points. And then chaotic researching is applied to jump out of local optimum. Simulation experimental results in wavelet extraction of synthetic seismogram and real seismic records show that this algorithm can get high precision in seismic wavelet extraction.