| 一种波速结构的两步优化反演策略 |
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| 引用本文： | 师黎静,陶夏新,赵纪生,路建波.一种波速结构的两步优化反演策略[J].地球物理学报,2009,52(8):2105-2112. |
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| 作者姓名： | 师黎静 陶夏新 赵纪生 路建波 |
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| 作者单位： | 1.中国地震局工程力学研究所，哈尔滨 150080;2.中国科学院研究生院地球科学学院，北京 100049;3.哈尔滨工业大学，哈尔滨 150090 |
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| 基金项目： | 国家自然科学基金，国家科技支撑计划项目，中国地震局工程力学研究所基本科研业务费专项 |
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| 摘 要： | 借助虚拟反演思路，通过对各种遗传算子不同匹配方式的比较研究，指出了对于频散曲线反演浮点数编码与轮盘赌选择的匹配方式离线性能最好，提出了一种两步优化反演策略.该两步策略利用浮点数编码、轮盘赌选择、浮点数均匀交换与变异算子匹配组成基本遗传算法框架，在此框架基础上施加免疫启发策略和免重复计算加速策略，多次运行，对每次运行结果继续施加模拟退火算法使其至少达到局部最优，最后取得最优解.免疫启发策略充分利用最佳个体的信息加速进化进程，通过对每代的最佳个体施加一服从标准正态分布的随机数来加强对邻近区域的局部搜索，通过标准差的调整也兼顾了对邻近区域以外区域的搜索，将局部搜索和全局搜索有机地结合起来，同时还最大限度地降低了对遗传算法自身进化进程的干扰；免重复计算策略大大减少了正演计算次数，节约了计算成本，提高了反演效率.两步反演策略避免了多次平均法的缺陷，提高了反演结果的稳定性和精度，降低了非惟一性.
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| 关 键 词： | 波速结构 遗传算法 免疫启发 模拟退火 |
| 收稿时间： | 2008-7-9 |
| 修稿时间： | 2008-9-27 |
A two-step optimization strategy for S-wave velocity structure inversion |
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| SHI Li-Jing,TAO Xia-Xin,ZHAO Ji-Sheng,LU Jian-Bo.A two-step optimization strategy for S-wave velocity structure inversion[J].Chinese Journal of Geophysics,2009,52(8):2105-2112. |
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| Authors: | SHI Li-Jing TAO Xia-Xin ZHAO Ji-Sheng LU Jian-Bo |
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| Institution: | 1.Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China;2.College of Earth Science, Graduate University of Chinese Academy of Sciences, Beijing 100049, China;3.Harbin Institute of Technology, Harbin 150090, China |
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| Abstract: | Various combinations of genetic operators are examined by the virtual inversion, as the result it is pointed out that the offline capability is best in dispersion curves inversion by combining float coding with roulette selection. One two-step inversion strategy is suggested by integrating the above two with arithmetical uniform crossover and mutation into a basic genetic inversion frame, and adding the immune heuristic method and the no-repeating forward calculation method to the above basic frame, then after several runs conducting Simulated Annealing Algorithm to the result of each individual run，finally the best of the local optimum is chosen as the final result. The immune heuristic method speeds up the evolution by adopting the best individual′s information, enhances the local search in its neighboring space by adding one random number with normal distribution, while giving attention to the search in other areas by rectifying the distribution deviation. The local search and global search are linked effectively; the disturbance to evolution of the genetic algorithm is minimized. By the no-repeating forward calculation, the times of forward calculations are reduced greatly, the calculating cost is saved and the evolution efficiency is increased. The shortcoming of averaging the results of all individual runs is avoided. The stability and precision of inversion is increased and the non-uniqueness is reduced. |
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| Keywords: | S-wave velocity structure Genetic Algorithm Immune heuristic Simulated Annealing Algorithm |
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