Arid
DOI10.1016/j.jhydrol.2017.12.071
A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model
Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor
通讯作者Chen, Mingjie
来源期刊JOURNAL OF HYDROLOGY
ISSN0022-1694
EISSN1879-2707
出版年2018
卷号557页码:826-837
英文摘要

Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol’ method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system. (C) 2018 Elsevier B.V. All rights reserved.


英文关键词MODFLOW Bayesian Inverse modeling Surrogate Bagging MARS
类型Article
语种英语
国家Oman
收录类别SCI-E
WOS记录号WOS:000425077300064
WOS关键词MONTE-CARLO-SIMULATION ; SEAWATER INTRUSION ; GEOTHERMAL PROSPECT ; OPTIMIZATION ; CO2 ; UNCERTAINTY ; FRAMEWORK ; IMPACTS
WOS类目Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向Engineering ; Geology ; Water Resources
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/211048
作者单位Sultan Qaboos Univ, Water Res Ctr, Muscat, Oman
推荐引用方式
GB/T 7714
Chen, Mingjie,Izady, Azizallah,Abdalla, Osman A.,et al. A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model[J],2018,557:826-837.
APA Chen, Mingjie,Izady, Azizallah,Abdalla, Osman A.,&Amerjeed, Mansoor.(2018).A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model.JOURNAL OF HYDROLOGY,557,826-837.
MLA Chen, Mingjie,et al."A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model".JOURNAL OF HYDROLOGY 557(2018):826-837.
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