Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021WR030051 |
Efficient discretization-independent Bayesian inversion of high-dimensional multi-Gaussian priors using a hybrid MCMC | |
Sebastian Reuschen; Fabian Jobst; Wolfgang Nowak | |
2021-07-14 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | In geostatistics, Gaussian random fields are often used to model heterogeneities of soil or subsurface parameters. To give spatial approximations of these random fields, they are discretized. Then, different techniques of geostatistical inversion are used to condition them on measurement data. Among these techniques, Markov chain Monte Carlo (MCMC) techniques stand out, because they yield asymptotically unbiased conditional realizations. However, standard Markov Chain Monte Carlo (MCMC) methods suffer the curse of dimensionality when refining the discretization. This means that their efficiency decreases rapidly with an increasing number of discretization cells. Several MCMC approaches have been developed such that the MCMC efficiency does not depend on the discretization of the random field. The pre-conditioned Crank Nicolson Markov Chain Monte Carlo (pCN-MCMC) and the sequential Gibbs (or block-Gibbs) sampling are two examples. This paper presents a combination of both approaches with the goal to further reduce the computational costs. Our algorithm, the sequential pCN-MCMC, will depend on two tuning-parameters: the correlation parameter of the pCN approach and the block size of the sequential Gibbs approach. The original pCN-MCMC and the Gibbs sampling algorithm are special cases of our method. We present an algorithm that automatically finds the best tuning-parameter combination ( and ) during the burn-in-phase of the algorithm, thus choosing the best possible hybrid between the two methods. In our test cases, we achieve a speedup factors of over pCN and of over Gibbs. Furthermore, we provide the MATLAB implementation of our method as open-source code. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/333830 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Sebastian Reuschen,Fabian Jobst,Wolfgang Nowak. Efficient discretization-independent Bayesian inversion of high-dimensional multi-Gaussian priors using a hybrid MCMC[J]. Water Resources Research,2021. |
APA | Sebastian Reuschen,Fabian Jobst,&Wolfgang Nowak.(2021).Efficient discretization-independent Bayesian inversion of high-dimensional multi-Gaussian priors using a hybrid MCMC.Water Resources Research. |
MLA | Sebastian Reuschen,et al."Efficient discretization-independent Bayesian inversion of high-dimensional multi-Gaussian priors using a hybrid MCMC".Water Resources Research (2021). |
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