基于混合粒子群的核磁共振反演算法研究  

Magnetic resonance sounding inversion algorithm based on hybrid particle swarm optimization

作　　者：宋鹏[1] 王国富[1] 

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541000

出　　处：《大众科技》2013年第12期71-73,共3页Popular Science & Technology

基　　金：国家自然基金(61102115);广西自然基金(2012GXNSFBA053177)

摘　　要：传统的基于最小方差原理的反演结果依赖于初始模型选择,易陷入局部极小,针对以上问题,文章利用完全非线性反演方法—粒子群反演算法,对核磁共振探测地下水的数据资料进行反演解释,该算法具有操作简单,并行处理,不要求被优化的目标函数具有可微、可导、连续等性质的优点。将基本粒子群算法与模拟退火算法结合,加入非线性约束优化条件,使其适用于核磁共振探测地下水数据资料的反演解释。试验结果表明,混合粒子群反演算法反演结果精度较高,收敛速度较快,验证了粒子群优化算法在核磁共振反演应用中的可行性。The inversion results based on the traditional principle of minimum variance depend on the initial model selection, which fall into local minimum easily.To solve the above problems,the particle swarm optimization algorithm, one of the perfect nonlinear inversions is used in inverse interpretation of magnetic resonance sounding data.This algorithm has advantages of operation sample ,parallel processing ,and no requirement of optimized target function to be differentiable,derivable and continuous.Particle swarm optimization with nonlinear constrained optimization is combined with simulated annealing,to be applied in inverse interpretation of magnetic resonance sounding data.Results of the trial indicated that the inversion result accuracy of hybrid particle swarm and the rate of convergence are relatively high,and verified the feasibility of particle swarm optimization applying to the inversion of Magnetic resonance sounding .

关 键 词：粒子群 模拟退火 非线性反演 约束优化 核磁共振 

分 类 号：P631[天文地球—地质矿产勘探]