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Volume 38 Issue 1
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CHENG Fei, LIU Jiang-ping, MAO Mao, WANG Jing, SONG Xian-hai. Self-adapting control parameters-based differential evolution algorithm for inversion of Rayleigh wave dispersion curves[J]. Chinese Journal of Geotechnical Engineering, 2016, 38(1): 147-154. DOI: 10.11779/CJGE201601016
Citation: CHENG Fei, LIU Jiang-ping, MAO Mao, WANG Jing, SONG Xian-hai. Self-adapting control parameters-based differential evolution algorithm for inversion of Rayleigh wave dispersion curves[J]. Chinese Journal of Geotechnical Engineering, 2016, 38(1): 147-154. DOI: 10.11779/CJGE201601016
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Self-adapting control parameters-based differential evolution algorithm for inversion of Rayleigh wave dispersion curves

  • 1. Hubei Subsurface Multi-scale Imaging Key Laboratory, China University of Geosciences (Wuhan), Wuhan 430074, China;
    2. CCCC Second Harbor Consultants Co., Ltd., Wuhan 430071, China

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  • Received Date: January 04, 2015
  • Published Date: January 19, 2016
    Graphical Abstract
    • Abstract
  • The differential evolution algorithm has been widely used in geophysical inversion including inversion of Rayleigh wave dispersion curves. At present the traditional differential evolution algorithm is sensitive to the control parameters set in the process of inversion of Rayleigh wave dispersion curves, and improper selection of the parameters will make the inversion results untrue. Based on the traditional differential evolution algorithm applied in the inversion of Rayleigh wave dispersion curves, the two control parameters, namely crossover probability and zoom factor, are directly coded to individuals, and the differential evolution algorithm with self-adapting control parameters in the inversion of high-frequency Rayleigh wave dispersion curves is adopted to obtain near-surface shear -wave velocity profiles. The results from both synthetic and actual field data demonstrate that: (1) The proposed algorithm not only inherits the simple and efficient features of standard differential evelution algorithm, but also can automatically pick proper parameter values for correct inversion iteration in the inversion of dispersion curves, without relying on the crossover control parameter and amplification factor of the difference vector. (2) The objective function in the proposed algorithm is proved to be able to rapidly converge to the global optimization solution. (3) The wide probability distribution of model parameters, which means the proposed algorithm can define the scope of true-value and find the global minimum even in an extensive search space and guarantee the reliability of inversion results. The proposed algorithm can be applied effectively in the inversion of Rayleigh wave dispersion curves.
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    • References (20)
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