气候变化领域集成服务门户(CCPortal): Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC

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DOI	10.1016/j.advwatres.2020.103614
	Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC
	Reuschen S.; Xu T.; Nowak W.
发表日期	2020
ISSN	0309-1708
卷号	141
英文摘要	The feasibility of probabilistic Bayesian inversion strongly depends on the dimensionality and complexity of the statistical prior model. Most geostatistical inversion approaches assume multi-Gaussian fields, and some assume (non-Gaussian) categorical fields, e.g., via multiple-point geostatistics. We combine these two into one hierarchical joint problem, which accounts for two (and possibly more) categories as well as heterogeneities inside each category. Recent works developed the conditional probability field method based on the Ensemble Kalman filter (EnKf) for this scenario. However, EnKf-type approaches take implicit linearity and (trans-)Gaussian assumptions, which are not feasible in weak-information regimes. Therefore, we develop a tailored Gibbs sampler, a kind of Markov chain Monte Carlo (MCMC) method. It can do this inversion without assumptions. Our algorithm extends an existing Gibbs sampler with parallel tempering for categorical fields to account for multi-Gaussian internal heterogeneity. We show our key idea and derive our algorithm from the detailed balance, required for MCMC algorithms. We test our algorithm on a synthetic channelized flow scenario for different levels of data available: A highly informative setting (transient flow data) where the synthetic truth can be recovered and a weakly informative setting (steady-state data only) where the synthetic truth cannot be recovered. Instead, we obtain a multi-modal posterior. For the proper testing of convergence, we use the scale reduction factor by Gelman and Rubin. Overall, the test illustrates that our algorithm performs well in both settings. © 2020
关键词	Markov chains Monte Carlo methods Tempering Conditional probabilities Ensemble Kalman Filter Gaussian assumption Geostatistical inversion Geostatistical models Markov chain Monte Carlo method Multiple-point geostatistics Parallel tempering Gaussian distribution Bayesian analysis Gaussian method geostatistics Kalman filter Markov chain Monte Carlo analysis steady-state equilibrium
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131796
推荐引用方式 GB/T 7714	Reuschen S.,Xu T.,Nowak W.. Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC[J]. Advances in Water Resources,2020,141.
APA	Reuschen S.,Xu T.,&Nowak W..(2020).Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC.,141.
MLA	Reuschen S.,et al."Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC".141(2020).