| 基于改进粒子群算法的地震标量波方程反演 |
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| 引用本文： | 朱童,李小凡,李一琼,张美根.基于改进粒子群算法的地震标量波方程反演[J].地球物理学报,2011,54(11):2951-2959. |
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| 作者姓名： | 朱童 李小凡 李一琼 张美根 |
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| 作者单位： | 1. 中国科学院地球深部重点实验室,中国科学院地质与地球物理研究所,北京 100029;
2. 中国石油化工股份有限公司石油物探技术研究院,南京 210014;
3. 中国地震局地球物理研究所,北京 100081 |
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| 基金项目： | 国家自然科学基金重点项目(40437018)、国家自然科学基金(40874024)、国家重点基础研究发展计划(973计划)(2007CB209603)资助. |
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| 摘 要： | 针对标准粒子群优化(PSO)算法存在易出现早熟而陷入局部最优以及进化后期收敛速度慢等缺陷,通过考虑粒子所处位置间相互作用,提出了一种改进的并行粒子群优化算法.由于引入粒子位置间的相互影响,减少了粒子搜索过程盲目性,因此能有效提高算法的收敛速度.数值试验表明,这种改进的粒子群算法适用于二维标量波方程的速度反演,且算法具有...
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| 关 键 词： | 改进粒子群算法 标量波方程 速度反演 并行 |
| 收稿时间： | 2010-10-16 |
Seismic scalar wave equation inversion based on an improved particle swarm optimization algorithm |
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| ZHU Tong,LI Xiao-Fan,LI Yi-Qiong,ZHANG Mei-Gen.Seismic scalar wave equation inversion based on an improved particle swarm optimization algorithm[J].Chinese Journal of Geophysics,2011,54(11):2951-2959. |
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| Authors: | ZHU Tong LI Xiao-Fan LI Yi-Qiong ZHANG Mei-Gen |
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| Institution: | 1. Key Laboratory of the Earth's Deep Interior,CAS, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029,China;
2. SINOPEC Geophysical Research Institute, Nanjing 210014, China;
3. Institute of Geophysics, China Earthquake Administration, Beijing 100081,China |
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| Abstract: | In the standard particle swarm optimization (PSO), the premature convergence of particles and slow convergence in the late process decrease the searching ability of the algorithm. In this paper, by taking the positions of the particles into consideration, we propose an improved parallel particle swarm optimization (IPPSO) algorithm, which can increase the efficiency, to reduce the blindness in the search process. The performance of this improved particle swarm optimization method in solving 2-D scalar wave equation inversion problems is investigated. Our numerical experiments indicate that this method is suitable for scalar velocity inversion problems since it has merits such as low dependence on initial model, high constringency speed, stable result and strong antinoise ability. The results hold promise for further elastic wave equation and elastic parameters inversion studies. |
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| Keywords: | Improved particle swarm optimization Scalar wave equation Velocity inversion Parallel |
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