基于粒子群优化算法的波阻抗反演研究
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摘要
波阻抗反演属于最优化问题,其目标函数可能包含多个在一定范围内连续的变量。传统的优化手段对某些函数存在难以优化,容易陷入局部解,收敛速度缓慢等问题。粒子群算法只考虑目标函数,对初始模型的依赖程度不高,可以随机地在全局域内进行搜索。通过分析粒子群算法的原理,提出了地震波阻抗反演粒子群算法的实现方法。详细地分析了粒子群算法的抗噪能力及算法中各参数对反演结果的影响,得出参数的最优组合。利用模型数据对该方法进行检测,在无噪情况下,反演结果与模型一致;在加入3%,10%和25%的噪声后,反演前后的相关系数分别为98.31%,93.27%和82.09%,证明了该方法的有效性。
Wave impedance inversion is indispensable in predicting reservoir properties.Wave impedance inversion is an optimization problem,and its objective function may include several continuous variables.In traditional optimization methods,the objective function is sometimes hard to solve.It is easy to fall into local extrema,and slow to converge.To cope with the problems,particle swarm optimization is developed.The method is weakly dependent on the initial model,and it searches randomly for the optimal solution in the global space.Based on an analysis of the principles of particle swarm algorithms,we put forward a seismic inversion method based on particle swarm optimization.We investigated in detail the effect of different parameters on inversion result,and an optimal combination of parameters was determined.We also discussed the noise resistance ability of the method,and tested it with model data.The inverted results coincide with noise-free theoretical data,and the correlation coefficient is 98.31%,93.27%,and 82.09% respectively when 3%,10%,or 25% noise is added to the original data.
Wave impedance inversion is indispensable in predicting reservoir properties.Wave impedance inversion is an optimization problem,and its objective function may include several continuous variables.In traditional optimization methods,the objective function is sometimes hard to solve.It is easy to fall into local extrema,and slow to converge.To cope with the problems,particle swarm optimization is developed.The method is weakly dependent on the initial model,and it searches randomly for the optimal solution in the global space.Based on an analysis of the principles of particle swarm algorithms,we put forward a seismic inversion method based on particle swarm optimization.We investigated in detail the effect of different parameters on inversion result,and an optimal combination of parameters was determined.We also discussed the noise resistance ability of the method,and tested it with model data.The inverted results coincide with noise-free theoretical data,and the correlation coefficient is 98.31%,93.27%,and 82.09% respectively when 3%,10%,or 25% noise is added to the original data.
引文
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15姚姚.地球物理反演———基本理论与应用方法[M].武汉:中国地质大学出版社,2002.72~82
2 Kennedy J,Eberhart R.Perth particle swarm optimiza-tion[A].In:IEEE.Proceedings IEEE internationalconference on neural networks[C].Piscataway:IEEEService Center,1995.1 942~1 948
3 Eberharb R C,Shi YH.Comparison between geneticalgorithms and particle swarm optimization[A].In:Porto V W,Saravanan N,Waagen D,et al.Evolution-ary programming[C].Berlin:Springer,1998.611~616
4 Rechenberg I.Evolution strategy[A].In:Zurada JM,MarksⅡR J,Robinson C J.Computational intel-ligence:Imitating life[C].Piscataway:IEEE Press,1994.147~159
5谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):134
6杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,6(5):87~94
7 Parsopoulos K E,Vrahatis M N.Particle swarm opti-mizer in noisy and continuously changing environments[A].In:Hamza M H.Artificial intelligence and softcomputing[C].ICancun,Mexico:IASTED/ACTAPress,2001.289~294
8 Parsopoulos K E,Vrahatis M N.Particle swarm opti-mization method for constrained optimization problems[A].In:IEEE.Proceedings of the Euro-lnternationalsymposium on computational intelligence[C].Slovaki-a:IOS Press,2002.2~7
9 Eberhart R C,Hu X.Human tremor analysis usingparticle swarm optimization[A].In:Anon.Proceed-ings of the IEEE congress on evolutionary computation[C].Washington:[s.n.],1999.1 927~1 930
10 Yoshida H,Kawata K,Fukuyama Y,et al.A particleswarm optimization for reactive power and voltage con-trol considering voltage security assessment[J].IEEETransactions on Power Systems,2000,15(4):1 232~1 239
11杨文采.地球物理反演的理论与方法[M].北京:地质出版社,1997.11~35
12伊成,谢桂生,吕中育,等.地震反演与非线性随机优化方法[J].物探化探计算技术,2001,23(1):6~10
13王山山,李灿平,李青仁,等.快速模拟退火地震反演[J].地球物理学报,1995,38(增刊1):123~134
14张永刚.地震波阻抗反演技术的现状和发展[J].石油物探,2002,41(4):385~390
15姚姚.地球物理反演———基本理论与应用方法[M].武汉:中国地质大学出版社,2002.72~82
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