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Volume 37 Issue 2
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FU Dai-guang, LIU Jiang-ping, ZHOU Li-ming, XU Hao, LIAO Jin-fang, CHEN Song, GUO Dao-long. Inversion of multimode Rayleigh-wave dispersion curves of soft interlayer based on Bayesian theory[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(2): 321-329. DOI: 10.11779/CJGE201502016
Citation: FU Dai-guang, LIU Jiang-ping, ZHOU Li-ming, XU Hao, LIAO Jin-fang, CHEN Song, GUO Dao-long. Inversion of multimode Rayleigh-wave dispersion curves of soft interlayer based on Bayesian theory[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(2): 321-329. DOI: 10.11779/CJGE201502016
shu

Inversion of multimode Rayleigh-wave dispersion curves of soft interlayer based on Bayesian theory

  • 1. Key Laboratory Geotechnical Mechanics and Engineering of the Ministry of Water Resources, Changjiang Scientific Research Institute, Wuhan 430010, China; 2. China University of Geosciences, Wuhan 430074, China; 3. Changjiang Scientific Research Institute, Wuhan 430010, China; 4. Hubei Shale Gas Development Co., Ltd., Wuhan 430071, China; 5. Wuhan Center of Geological Survey, China Geological Survey, Wuhan 430502, China; 6. Hydrogeology and Engineering Geology Prospecting Institute of Heilongjiang Province, Harbin 150030, China

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  • Received Date: August 03, 2014
  • Published Date: March 01, 2015
    Graphical Abstract
    • Abstract
  • Obtaining shear-wave velocity and thickness of soft interlayer with higher precision is always one of the difficulties in inversion of Rayleigh-wave dispersion curve, and it is not obviously improved when only depending on the improved algorithm and multimode inversion for low-sensitivity soft interlayer. The improved algorithm and combination of multimode and nonlinear Bayes' theorem are adopted to invert low-sensitivity soft interlayer. The damping inertia weight and chaos are added into the particle swarm optimization as improved algorithm. However, the improved algorithm does not solve the problem with low-sensitivity soft interlayer models. To analyze and evaluate the factors affecting the accuracy of inversion from the perspective of the inversion solution, the unbiased Metropolis-Hastings sampling (MHS) method is used for numerical integration posterior probability, and the rotation of parameters is used to improve the efficiency of sampling. The obtained integral 1D and mixed marginal probability distributions and correlation sufficiend matrix of parameters reflect the uncertainty and parameter inversion solution for correlation and other information. To solve the problem of low-curacy inversion of low-sensitivity soft interlayer, the Bayesian information criterion (BIC) is employed to determine the optimal parameters of the model. The optimal model agrees with the
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    • References (24)
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