Title: Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. 2. Quantifying the Uncertainty

Abstract

Abstract 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 demonstratedmore » 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.« less

Authors:

Alghamdi, Amal  ^[1];

• Search DOE PAGES for author "Alghamdi, Amal"
• Search DOE PAGES for ORCID "0000-0003-0145-5296"

Hesse, Marc A.  ^[1];

• Search DOE PAGES for author "Hesse, Marc A."
• Search DOE PAGES for ORCID "0000-0002-2532-3274"

Chen, Jingyi  ^[1];

• Search DOE PAGES for author "Chen, Jingyi"
• Search DOE PAGES for ORCID "0000-0001-8493-6938"

Villa, Umberto ^[2]; Ghattas, Omar ^[1]

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+ Show Author Affiliations

1. University of Texas, Austin, TX (United States)
2. Washington University, St. Louis, MO (United States)

Publication Date:

Fri Aug 06 00:00:00 EDT 2021

Research Org.:

Univ. of Texas, Austin, TX (United States)

Sponsoring Org.:

USDOE Office of Science (SC); National Science Foundation (NSF); Ministry of Education Saudi Arabia

OSTI Identifier:

1978568

Alternate Identifier(s):

OSTI ID: 1832644

Grant/Contract Number:  

SC0019303; CBET-1508713; ACI-1550593; DMS-2012453; DE‐SC0019303

Resource Type:

Accepted Manuscript

Journal Name:

Water Resources Research

Additional Journal Information:

Journal Volume: 57; Journal Issue: 11; Journal ID: ISSN 0043-1397

Publisher:

American Geophysical Union (AGU)

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES