基于遗传算法的弯曲射线成像反演
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摘要
介质体反演成像一直是困扰地球物理等领域的重要问题 ,尤其是它的计算速度、成像质量和稳定性等更是备受关注 .首先简述了基于弯曲射线成像的基本原理 ,列出了反演的详细步骤 ,构建了用于反演的数学模型 ;然后提出了利用改进的遗传算法解决弯曲射线成像中的反演问题 ,并给出了改进的遗传算法步骤 ;最后给出了一个用改进的遗传算法成像的例子 ,同时为了突出该算法的优势 ,还把它与爬山法相比 ,从两者算法的本质区别剖析了两者反演结果的差别 ,指出 :正是遗传算法大规模的并行搜索以及杂交与变异的约束 ,导致了成像质量及速度的不同 ;通过它们的反演迭代运算曲线图可以看出 ,该算法有效地提高了成像的速度、质量和稳定性
Medium reverse figure imaging is still an important problem that puzzles the fields such as earth physics, and especially people focus their eyes on the computer velocity, imaging quality and its stability. First, basic principle of bending rays imaging is discussed briefly in the beginning of the paper, then the reverse figure steps are given in detail, mathematical model for reverse figure is designed too. This paper also presents an improved genetic algorithm to settle the reverse figure of bending rays imaging, and its steps are also displayed out. At the end of the paper, an example that gained by improved genetic algorithm is presented, and in order to pop out its advantages, climb hill algorithm is also compared with it. We analyze the difference of the results from their essences and also point out that the large scale collateral calculation and the restriction of crossbreed and aberrance that lead to the velocity and quality. We can conclude the algorithm improves its velocity, quality and stability efficiently by the curve figures of the improved genetic algorithm and climb hill algorithm.
Medium reverse figure imaging is still an important problem that puzzles the fields such as earth physics, and especially people focus their eyes on the computer velocity, imaging quality and its stability. First, basic principle of bending rays imaging is discussed briefly in the beginning of the paper, then the reverse figure steps are given in detail, mathematical model for reverse figure is designed too. This paper also presents an improved genetic algorithm to settle the reverse figure of bending rays imaging, and its steps are also displayed out. At the end of the paper, an example that gained by improved genetic algorithm is presented, and in order to pop out its advantages, climb hill algorithm is also compared with it. We analyze the difference of the results from their essences and also point out that the large scale collateral calculation and the restriction of crossbreed and aberrance that lead to the velocity and quality. We can conclude the algorithm improves its velocity, quality and stability efficiently by the curve figures of the improved genetic algorithm and climb hill algorithm.
引文
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2 IvanssonS,BoreholeSeismic.Tom ography-theory andcomputational methods[J].Proceeding ofIEEE,1986,24(2):328～338.
3 马宁.由不完全投影数据重建图象的算法研究[博士学位论文][D].武汉:武汉大学,1997:24～30.
4 HollandJH.Adaptation in natural and artificial system[M].Michigan:TheU niversity ofMichiganPress,1975:2～114.
5 康立山,潘正君.演化计算[M].北京:清华大学出版社,1998:1～35.
6 PanZ J,KangL S.Evolving both the topology and weights ofneural networks[J].ParallelAlgorithms andApplications,1996,9(2):299～307
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