自适应混沌嵌入式粒子群算法提取地震子波  

Seismic wavelet extraction based on adaptive chaotic embedded particle swarm optimization algorithm

作　　者：戴永寿[1] 魏玉琴 张亚南[1] 陈健[1] 丁进杰[1] 

机构地区：[1]中国石油大学(华东)信息控制与工程学院,山东青岛266580 [2]中冶东方工程技术有限公司,山东青岛266555

出　　处：《石油地球物理勘探》2013年第6期896-902,1016+848-849,共7页Oil Geophysical Prospecting

基　　金：国家自然科学基金项目(40974072);山东省自然科学基金项目(ZR2010DM14)联合资助

摘　　要：本文提出一种基于Tent映射的自适应混沌嵌入式粒子群算法,通过拟合优化求解目标函数得到子波模型参数。算法将混沌变量嵌入到标准粒子群算法中,且对惯性权重和学习因子进行非线性自适应调整,并建立平均粒距与适应度方差相结合的早熟判断机制,以混沌搜索的方式跳出局部最优。合成地震数据仿真及实际地震数据处理结果表明,该算法应用于地震子波提取具有较高的求解精度。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.

关 键 词：地震子波提取 混沌 粒子群算法 累积量拟合 

分 类 号：P631[天文地球—地质矿产勘探]