数据和知识融合的Bayesian-MCMC三维地质建模

王丽芳; 刘肖莉; 徐坤; 杜林泽; 徐章皓; 张宝一

doi:10.3799/dqkx.2023.069

Volume 49 Issue 8

Aug. 2024

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Article Navigation > Earth Science > 2024 > 49(8): 3056-3070

Wang Lifang, Liu Xiaoli, Xu Kun, Du Linze, Xu Zhanghao, Zhang Baoyi, 2024. Bayesian-MCMC (Markov Chain Monte Carlo) Based Three-Dimensional Geological Model Optimization by Data and Knowledge Fusion. Earth Science, 49(8): 3056-3070. doi: 10.3799/dqkx.2023.069

Citation:

Wang Lifang, Liu Xiaoli, Xu Kun, Du Linze, Xu Zhanghao, Zhang Baoyi, 2024. Bayesian-MCMC (Markov Chain Monte Carlo) Based Three-Dimensional Geological Model Optimization by Data and Knowledge Fusion. Earth Science, 49(8): 3056-3070. doi: 10.3799/dqkx.2023.069

Citation:

Wang Lifang, Liu Xiaoli, Xu Kun, Du Linze, Xu Zhanghao, Zhang Baoyi, 2024. Bayesian-MCMC (Markov Chain Monte Carlo) Based Three-Dimensional Geological Model Optimization by Data and Knowledge Fusion. Earth Science, 49(8): 3056-3070. doi: 10.3799/dqkx.2023.069

PDF( 12244 KB)

Bayesian-MCMC (Markov Chain Monte Carlo) Based Three-Dimensional Geological Model Optimization by Data and Knowledge Fusion

doi: 10.3799/dqkx.2023.069

Wang Lifang^{1, 2, 3
,
,},
Liu Xiaoli^{3, 4},
Xu Kun⁴,
Du Linze⁴,
Xu Zhanghao⁴,
Zhang Baoyi^{1, 4
,
,
,}

1.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China
2.
School of Geomatics & Geography, Hunan Vocational College of Engineering, Changsha 410151, China
3.
Wuhan ZGIS Science & Technology Co., Ltd., Wuhan 430074, China
4.
School of Geosciences & Info-Physics, Central South University, Changsha 410083, China

Received Date: 2023-01-22
Available Online: 2024-08-27
Publish Date: 2024-08-25

Abstract

Abstract

To fully utilize known knowledge to reduce uncertainty of three-dimensional (3D) geological model, a Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) based 3D geological model inference framework is proposed to consider the uncertainty of modeling data (prior parameter) and integrate known knowledge or geophysical exploration data as likelihood function into the process of 3D geological implicit modeling by Cokriging interpolant, which well satisfies the known knowledge. Firstly, different prior distributions of modeling data and likelihood functions of known knowledge are built based on Bayesian probabilistic theory. Secondly, the posterior space of prior parameter (modeling data) can be explored by Markov chain Monte Carlo (MCMC) sampling method to obtain a large number of samples which simultaneously satisfy the prior distributions of modeling data and likelihood constraints of know knowledge. Thirdly, a series of 3D geological model realizations satisfying known knowledge can be constructed by Cokriging interpolant using above newly obtained samples of modeling data. Finally, compared with the deterministic 3D geological modeling methods, the Bayesian-MCMC probabilistic inference framework can obtain a series of model realizations to allowably apply information entropy to evaluate model uncertainty. Taking the Lingnian-Nacha area in the southwestern Guangxi Zhuang Autonomous Region (GZAR) of China as am example, the proposed Bayesian-MCMC probabilistic inference framework was used to optimize 3D geological model simultaneously considering location and attitude uncertainties of strata and fault and known knowledge of stratum thickness and attitudes of strata and fault. The results of case study show that the proposed method can not only reconstruct 3D spatial geometry of geological body but also reduce uncertainty of 3D geological model, which provides geologist with an effective way to integrate modeling data and known knowledge to reconstruct 3D geological model and reduce its uncertainty.
- implicit modeling,
- Bayes,
- MCMC,
- uncertainty,
- cokriging interpolant

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References(39)

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