| 地球物理资料群体智能反演(英文) |
| |
| 引用本文： | 袁三一,王尚旭,田楠.地球物理资料群体智能反演(英文)[J].应用地球物理,2009,6(2):166-174. |
| |
| 作者姓名： | 袁三一 王尚旭 田楠 |
| |
| 作者单位： | CNPC Key Laboratory of Geophysical Exploration,Key Laboratory of Earth Prospecting and Information Technology,China University of Petroleum,Beijing 102249,China
|
| |
| 基金项目： | 国家重点基础研究发展规划(973计划)，Open Fund |
| |
| 摘 要： | 复杂地球物理资料的反演问题往往是一个求解多参数非线性多极值的最优解问题。而鸟和蚂蚁等群体觅食的过程,正好与寻找地球物理反演最优解的过程相似。基于自然界群体协调寻优的思想,本文提出了交叉学科的群体智能地球物理资料反演方法,并给出了其对应的数学模型。用一个有无限多个局部最优解的已知模型对该类方法进行了试验。然后,将它们应用到了不同的复杂地球物理反演问题中:(1)对噪声敏感的线性问题;(2)非线性和线性同步反演问题;(3)非线性问题。反演结果表明,群体智能反演是可行的。与常规遗传算法和模拟退火法相比,该类方法有收敛速度相对快、收敛精度相对高等优点;与拟牛顿法和列文伯格一马夸特法相比,该类方法有能跳出局部最优解等优点。
|
| 关 键 词： | 应用地球物理 数据反演 智能优化 非线性问题 粒子群优化算法 群体 地球物理数据 优化问题 |
| 收稿时间： | 4 March 2002 |
Swarm intelligence optimization and its application in geophysical data inversion |
| |
| Sanyi Yuan,Shangxu Wang,Nan Tian.Swarm intelligence optimization and its application in geophysical data inversion[J].Applied Geophysics,2009,6(2):166-174. |
| |
| Authors: | Sanyi Yuan Shangxu Wang Nan Tian |
| |
| Institution: | CNPC Key Laboratory of Geophysical Exploration, Key Laboratory of Earth Prospecting and Information Technology, China University of Petroleum, Beijing 102249, China |
| |
| Abstract: | The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems: (1) a linear problem which is sensitive to noise, (2) a synchronous inversion of linear and nonlinear problems, and (3) a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions. |
| |
| Keywords: | Swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
|
