| 基于子空间的二维大地电磁量子遗传反演法研究 |
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| 引用本文： | 范建柯,师学明,吴时国,施剑.基于子空间的二维大地电磁量子遗传反演法研究[J].地球物理学报,2011,54(10):2682-2689. |
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| 作者姓名： | 范建柯 师学明 吴时国 施剑 |
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| 作者单位： | 1. 中国科学院海洋研究所海洋地质与环境重点实验室,青岛 266071;
2. 中国科学院研究生院,北京 100049;
3. 中国地质大学(武汉)地球物理与空间信息学院,武汉 430074;
4. 国土资源部海洋油气与环境地质重点实验室,青岛 266071;
5. 青岛海洋地质研究所,青岛 266071 |
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| 基金项目： | 湖北省自然科学基金计划青年杰出人才项目，中国海陆地质地球物理系列图项目 |
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| 摘 要： | 量子遗传算法作为一种高效的优化算法,仍存在容易陷入局部极值的缺点.为提高算法的高效性,并探讨将算法应用于大地电磁二维反演的可行性和有效性,本文对算法进行了改进,并通过一维两层D型和四层HK型模型数值试验验证了改进的有效性.然后将改进后的算法引入二维大地电磁反演,在引入滑动子空间思想,同时只考虑最简化反演条件的前提下,对...
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| 关 键 词： | 大地电磁 二维反演 量子遗传算法 滑动子空间 |
| 收稿时间： | 2010-12-06 |
A study of 2-D magnetotelluric quantum genetic inversion algorithm based on subspace |
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| FAN Jian-Ke,SHI Xue-Ming,WU Shi-Guo,SHI Jian.A study of 2-D magnetotelluric quantum genetic inversion algorithm based on subspace[J].Chinese Journal of Geophysics,2011,54(10):2682-2689. |
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| Authors: | FAN Jian-Ke SHI Xue-Ming WU Shi-Guo SHI Jian |
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| Institution: | 1. Key Laboratory of Marine Geology and Environment,Institute of Oceanology, Chinese Academy of Sciences,Qingdao 266071,China;
2. Graduate University of the Chinese Academy of Sciences,Beijing 100049, China;
3. Institute of Geophysics & Geomatics,China University of Geosciences,Wuhan 430074, China;
4. The Key Laboratory of Marine Hydrocarbon Resource and Environmental Geology,Ministry of Land and Resources,Qingdao 266071, China;
5. Qingdao Institute of Marine Geology,Qingdao 266071,China |
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| Abstract: | Quantum Genetic Algorithm is an excellent method. Nevertheless, there is a disadvantage to trap into the local minima for the conventional Quantum Genetic Algorithm. To advance the algorithm and probe the feasibility and effectiveness of the algorithm introduced into the magnetotelluric data 2D inversion, some improvements are made, whose effectiveness is testified through the inversion for 1D magnetotelluric two-layer (D-type) model and four-layer (HK-type) model. Then,the improved method is introduced into the magnetotelluric data 2D inversion. Based on the sliding subspace and the most simplified inversion condition, one typical 2D low-resistivity model is inversed using the conventional Quantum Genetic Algorithm and the improved Quantum Genetic Algorithm, respectively. The results indicate that it is feasible and effective to apply the Quantum Genetic Algorithm to magnetotelluric 2D inversion based on the subspace method and the result from the improved method is better than the conventional method. Finally, the better result is also obtained for the field data. |
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| Keywords: | MT 2D inversion Quantum Genetic Algorithm Sliding subspace |
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