基于粒子群优化的自然电场数据反演  

Inversion of self-potential anomalies based on particle swarm optimization

作　　者：朱肖雄[1,2] 崔益安[1,2] 李溪阳[1,2] 佟铁钢[1,2] 纪铜鑫[1,2] 

机构地区：[1]中南大学地球科学与信息物理学院,湖南长沙410083 [2]中南大学有色资源与地质灾害探查湖南省重点实验室,湖南长沙410083

出　　处：《中南大学学报（自然科学版）》2015年第2期579-585,共7页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(41274122;41374119);教育部博士点基金资助项目(20110162130008);国家科技支撑计划项目(2011BAB04B08);国家科技基础性工作专项(2013FY110800)~~

摘　　要：在分析测试粒子数、速度因子、目标函数等算法参数对粒子群优化算法效果的影响规律的基础上,设计自然电场粒子群优化反演算法,并对加入不同程度白噪声模拟数据进行反演试算。研究结果表明:设计的粒子群优化算法能有效实现对自然电场数据的反演,算法具有收敛速度快、稳定、反演精度较高和抗噪音能力强等优点,可以较为准确地得到异常体的位置、形态、极化角等参数,能较好地满足生成实际要求。Based on testing and analyzing relevant parameters including particle quantity, rate scale factor, objective function etc., the particle swarm optimization （PSO） was used to design inversion algorithm for self-potential data. Through adding different degrees of Gauss noise, the synthetic data was used to test the designed inversion algorithm. The results show that the PSO algorithm can effectively realize the inversion of self-potential data with fast and stable convergence, high inversion accuracy and high anti-noise capability. Through the designed algorithm, the parameters contain origin of the anomaly, shape, polarization angle, etc, can relativey accurately be obtained and can meet the demands of engineering investigation and mineral exploration.

关 键 词：自然电场 反演 粒子群优化 参数分析 

分 类 号：P631[天文地球—地质矿产勘探]