粒子群算法反演密度异常的数值模拟研究  

Research on Numerical Simulation Inversion of PSO Algorithm Density Anomaly

作　　者：段虎荣[1,2] 徐海军[2] 刘明学[1] 

机构地区：[1]陕西铁路工程职业技术学院测绘工程系,渭南714000 [2]长安大学地质工程与测绘学院,西安710054

出　　处：《工程地球物理学报》2010年第5期544-547,共4页Chinese Journal of Engineering Geophysics

基　　金：国家自然科学基金(编号:40674001)资助

摘　　要：基于引力位理论推导出长方体外任意一点的重力异常公式,将模拟的研究区域划分成许多具有固定长、宽、高且密度均匀的长方体,并给定了每个长方体的密度异常,利用重力的可叠加性,计算了重力异常分布。在此基础上,采用粒子群算法进行密度反演试验,同时将粒子群算法与其它算法进行了比较,结果表明:在适应度值为0.99的条件下,粒子群算法比遗传算法耗时减少了93.7%。This paper firstly derives the gravity anomaly formula of any point outside the Cuboid according to the gravitational theory,and then divides the simulation study area into many cuboids with a fixed length,width,height and uniform density,and sets the density anomaly of each cuboid.Meanwhile,the distribution of gravity anomaly is calculated according to the superposition of gravity.Based on the above processes,the paper makes density inversion test by means of Particle Swarm Optimization（PSO） Algorithm,and compares the results with that obtained by Genetic Algorithm（GA）.It is proved that the time consumption of PSO Algorithm is decreased by 93.7% when the fitness value is 0.99.

关 键 词：粒子群算法 密度异常 数值模拟 重力异常 

分 类 号：P631[天文地球—地质矿产勘探]