层状介质大地电磁的自适应量子遗传反演法

师学明; 范建柯; 罗红明; 肖敏; 杨国世; 张旭辉

Volume 34 Issue 4

Jul. 2009

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Article Navigation > Earth Science > 2009 > 34(4): 691-698

SHI Xue-ming, FAN Jian-ke, LUO Hong-ming, XIAO Min, YANG Guo-shi, ZHANG Xu-hui, 2009. Adaptive Quantum Genetic Inversion Algorithm for One-Dimensional Magnetotelluric Inverse Problem. Earth Science, 34(4): 691-698.

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Adaptive Quantum Genetic Inversion Algorithm for One-Dimensional Magnetotelluric Inverse Problem

Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China

Received Date: 2008-07-25
Publish Date: 2009-07-25

Abstract

Abstract

This paper applied the conventional quantum genetic algorithm (QGA) to solve the nonlinear magnetotelluric inverse problem of layered model.However, the conventional QGA shows a premature convergence problem throughout our numerical experiments.In order to overcome the shortcoming of premature convergence, we improved the conventional QGA with automatically adjusting the size of model space with different scales, and eventually developed a novel method, referred as to adaptive quantum genetic algorithm (AQGA), for the inversion of magnetotelluric data.The validity of AQGA method is demonstrated by some optimization test functions and synthetic magnetotelluric models.The results show that AQGA mitigate the premature convergence and improve the efficiency and accuracy of inverted models.The obtained models using AQGA for magnetotelluric field data are well agreed with geological structure, which inferred that the improved AQGA method is powerful for the nonlinear optimization problem.
- adaptive,
- quantum genetic algorithm,
- MT,
- inversion,
- nonlinear optimization,
- geophysical prospecting.

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References(35)

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