基于自回归滑动平均模型和粒子群算法的地震子波提取  

Seismic wavelet extraction based on auto-regressive and moving average model and particle swarm optimization

作　　者：戴永寿[1] 牛慧[1] 彭星[1] 王少水[1] 

机构地区：[1]中国石油大学信息与控制工程学院,山东东营257061

出　　处：《中国石油大学学报（自然科学版）》2011年第3期47-50,57,共5页Journal of China University of Petroleum(Edition of Natural Science)

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)

摘　　要：基于自回归滑动平均(ARMA)模型理论,对地震子波进行参数化建模,采用累积量拟合法精确估计参数,使地震子波提取问题最终归结为一个多参数、多极值的非线性函数优化问题。对基本粒子群算法进行改进,通过自适应参数调整和边界约束,克服基本粒子群算法易陷入局部极值的缺陷,同时提高算法寻优精度和计算效率。仿真数据试验结果验证了改进的粒子群算法在地震子波提取方法中的有效性和稳定性。A seismic wavelet parametric model was developed based on auto-regressive and moving average（ARMA） model theory.The model parameters were accurately determined based on cumulant fitting method.So the seismic wavelet can be a multi-parameters,multi-extremes nonlinear functional optimization problem.An improved particle swarm optimization with adaptive parameters and boundary constraints was proposed for the local extreme value defects of elementary particle swarm optimization.The optimization accuracy and computation efficiency are also improved.Simulation results show that the method has good applicability and stability in seismic wavelet extraction.

关 键 词：地震数据处理 自回归滑动平均模型 地震子波 系统辨识 累积量拟合 粒子群算法 

分 类 号：P631[天文地球—地质矿产勘探]