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DOI | 10.1029/2021WR029775 |
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part II: Quantifying the Uncertainty | |
Amal Alghamdi; Marc A. Hesse; Jingyi Chen; Umberto Villa; Omar Ghattas | |
2021-08-06 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. Here we develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. We design a generalized preconditioned Crank–Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest. 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/335411 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Amal Alghamdi,Marc A. Hesse,Jingyi Chen,et al. Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part II: Quantifying the Uncertainty[J]. Water Resources Research,2021. |
APA | Amal Alghamdi,Marc A. Hesse,Jingyi Chen,Umberto Villa,&Omar Ghattas.(2021).Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part II: Quantifying the Uncertainty.Water Resources Research. |
MLA | Amal Alghamdi,et al."Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part II: Quantifying the Uncertainty".Water Resources Research (2021). |
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