基于贝叶斯理论面波频散曲线随机反演

doi:10.11720/wtyht.2021.1100

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Abstract

Surface wave dispersion curve inversion is an important geophysical method for obtaining the velocity and thickness distribution of underground shear wave.Conventional linear inversion methods,such as iterative least squares,relying on the initial model and multiple solution,are easy to fall into local minimum and low inversion accuracy.The stochastic inversion method based on Bayesian theory is a nonlinear inversion method which can integrate prior information.This method does not need initial model,only uses prior information to sample the model randomly,and selects and accepts the appropriate inversion model according to the probability distribution.It achieves the accurate estimation of the detail information.In this paper,the authors present a Bayesian Markov Monte Carlo (MCMC) stochastic inversion method based on GPR data constraints to invert the Rayleigh-waves dispersion curve.In the inversion process,by randomly changing the model parameters and calculating the likelihood function of the dispersion curve and the actual dispersion curve,researchers can choose whether to accept the new model parameters,repeat this process continuously,and finally get the best fitting result with the actual dispersion curve and the posterior probability density distribution of the VS solution.The typical numerical model test and field seismic data demonstrate that,compared with the conventional unconstrained stochastic inversion,the proposed method can effectively reduce the multiple solution and improve the efficiency and accuracy.

Key words： Bayesian theory Monte Carlo Markov Chain (MCMC) stochastic inversion Rayleigh wave dispersion curve constraint inversion

Received: 09 March 2020 Published: 20 August 2021

ZTFLH:

P631.4

Corresponding Authors: LI Jing E-mail: 177344303@qq.com;ljwy1209@163.com

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	Hui LIU
	Jing LI
	Zhao-Fa ZENG
	Tian-Qi WANG

Cite this article:

Hui LIU,Jing LI,Zhao-Fa ZENG, et al. Stochastic inversion of surface wave dispersion curves based on Bayesian theory[J]. Geophysical and Geochemical Exploration, 2021, 45(4): 951-960.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1100 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I4/951

Model parameterization using Voronoi nuclei
a—1 layer model without constraints;b—3 layer model with GPR constraints

Illustration of four possible perturbations to a current model
a—change v_s of a nuclcus;b—move a nuclcus to a different depth;c—give birth to a new floating nucleus;d—remove a floating nucleus

Basic flowchart of surface wave stochastic inversion

4-layer v_s incremental model parameters

Fundamental dispersion curve inversion of four-layer v_s increasing model
a—inversion results without constraints;b—inversion results with prior constraints;c—dispersion curves of the best fitting model and true model for unconstrained inversion;d—dispersion curves of the best fitting model and the true model with prior constraint inversion;e—misfit iteration curve

4-layer model with low velocity interlayer

Fundamental dispersion curve inversion of low-velocity layer model
a—stochastic inversion results without constraints;b—stochastic inversion results with prior constraints;c—dispersion curve of the best fitting model and true model for unconstrained inversion;d—error of dispersion curve for unconstrained inversion;e—dispersion curves of the best fitting model and the true model with prior constraint inversion;f—error of dispersion curve with prior constraint inversion;g—misfit iteration curve

Processed GPR profile(a)and GPR layer recognition results based on Convolutional Neural Networks(b)

Inversion results of real surface wave data
a—one shot gather of 13^th shot;b—extracted dispersion curve;c—unconstrained stochastic inversion result;d—stochastic inversion with GPR constrained;e—iterations

Comparison of quasi-two-dimensional profile inversion results
a—2D velocity profile of unconstrained stochastic inversion;b—2D velocity profile of GPR constrained stochastic inversion

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