Surrogate model based iterative ensemble smoother for subsurface flow   data assimilation

doi:10.1016/j.advwatres.2016.12.001

CORC > 北京大学 > 工学院

	Surrogate model based iterative ensemble smoother for subsurface flow data assimilation
	Chang, Haibin ; Liao, Qinzhuo ; Zhang, Dongxiao
刊名	ADVANCES IN WATER RESOURCES
	2017
关键词	Data assimilation Iterative ensemble smoother Surrogate model Independent parameters Subsurface flow PARTIAL-DIFFERENTIAL-EQUATIONS STOCHASTIC COLLOCATION METHOD RANDOM INPUT DATA KALMAN FILTER UNCERTAINTY QUANTIFICATION HYDRAULIC CONDUCTIVITY CONTAMINANT TRANSPORT PARAMETERS FIELDS NONSTATIONARY
DOI	10.1016/j.advwatres.2016.12.001
英文摘要	Subsurface geological formation properties often involve some degree of uncertainty. Thus, for most conditions, uncertainty quantification and data assimilation are necessary for predicting subsurface flow. The surrogate model based method is one common type of uncertainty quantification method, in which a surrogate model is constructed for approximating the relationship between model output and model input. Based on the prediction ability, the constructed surrogate model can be utilized for performing data assimilation. In this work, we develop an algorithm for implementing an iterative ensemble smoother (ES) using the surrogate model. We first derive an iterative ES scheme using a regular routine. In order to utilize surrogate models, we then borrow the idea of Chen and Oliver (2013) to modify the Hessian, and further develop an independent parameter based iterative ES formula. Finally, we establish the algorithm for the implementation of iterative ES using surrogate models. Two surrogate models, the PCE surrogate and the interpolation surrogate, are introduced for illustration. The performances of the proposed algorithm are tested by synthetic cases. The results show that satisfactory data assimilation results can be obtained by using surrogate models that have sufficient accuracy. (C) 2016 Elsevier Ltd. All rights reserved.; National Science and Technology Major Project of China [2016ZX05009005]; National Natural Science Foundation of China [U1262204, 51304008, 51520105005, U1663208]; Platform Construction Project for Researches on the Relationship between Water and Ecology in the Ordos Plateau [201311076]; SCI(E); ARTICLE; 96-108; 100
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/475285]
专题	工学院
推荐引用方式 GB/T 7714	Chang, Haibin,Liao, Qinzhuo,Zhang, Dongxiao. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation[J]. ADVANCES IN WATER RESOURCES,2017.
APA	Chang, Haibin,Liao, Qinzhuo,&Zhang, Dongxiao.(2017).Surrogate model based iterative ensemble smoother for subsurface flow data assimilation.ADVANCES IN WATER RESOURCES.
MLA	Chang, Haibin,et al."Surrogate model based iterative ensemble smoother for subsurface flow data assimilation".ADVANCES IN WATER RESOURCES (2017).