Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

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

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1029/2021wr029775· OSTI ID:1978568
 [1];  [2];  [2];  [3];  [2]
  1. University of Texas, Austin, TX (United States); Univ. of Texas at Austin, TX (United States)
  2. University of Texas, Austin, TX (United States)
  3. Washington University, St. Louis, MO (United States)
+ Show Author Affiliations

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. 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. Here 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.

Research Organization:
University of Texas, Austin, TX (United States)
Sponsoring Organization:
USDOE Office of Science (SC); National Science Foundation (NSF); Ministry of Education Saudi Arabia
Grant/Contract Number:
SC0019303
OSTI ID:
1978568
Alternate ID(s):
OSTI ID: 1832644
Journal Information:
Water Resources Research, Journal Name: Water Resources Research Journal Issue: 11 Vol. 57; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

References (39)

Joint inversion in coupled quasi-static poroelasticity: COUPLED POROELASTIC INVERSION journal February 2014
On the causes of pressure oscillations in low-permeable and low-compressible porous media: PRESSURE OSCILLATIONS IN LOW-PERMEABLE POROUS MEDIA
  • Haga, Joachim Berdal; Osnes, Harald; Langtangen, Hans Petter
  • International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 36, Issue 12 https://doi.org/10.1002/nag.1062
journal July 2011
Automated Solution of Differential Equations by the Finite Element Method book January 2012
Land subsidence in China journal July 2005
Groundwater depletion: A global problem journal February 2005
Groundwater-pumping optimization for land-subsidence control in Beijing plain, China journal January 2018
Overcoming the problem of locking in linear elasticity and poroelasticity: an heuristic approach journal December 2008
Recent progress on reservoir history matching: a review journal July 2010
Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain journal March 2013
On uncertainty quantification in hydrogeology and hydrogeophysics journal December 2017
A fully coupled 3-D mixed finite element model of Biot consolidation journal June 2010
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet journal September 2015
Dimension-independent likelihood-informed MCMC journal January 2016
Geometric MCMC for infinite-dimensional inverse problems journal April 2017
Three-dimensional deformation and strain induced by municipal pumping, part 1: Analysis of field data journal March 2006
Three-dimensional deformation and strain induced by municipal pumping, Part 2: Numerical analysis journal November 2006
On the importance of geological heterogeneity for flow simulation journal February 2006
Global depletion of groundwater resources: GLOBAL GROUNDWATER DEPLETION journal October 2010
Nonsustainable groundwater sustaining irrigation: A global assessment: NONSUSTAINABLE GROUNDWATER SUSTAINING IRRIGATION journal January 2012
Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate journal October 2020
A Reassessment of the Groundwater Inverse Problem journal May 1996
Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information journal February 1986
Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 3. Application to Synthetic and Field Data journal February 1986
Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 2. Uniqueness, Stability, and Solution Algorithms journal February 1986
The global groundwater crisis journal October 2014
General Theory of Three‐Dimensional Consolidation journal February 1941
Submillimeter Accuracy of InSAR Time Series: Experimental Validation journal May 2007
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach: Link between Gaussian Fields and Gaussian Markov Random Fields journal August 2011
The Influence of Geologic Structures on Deformation due to Ground Water Withdrawal journal March 2008
Inherent Limitations of Hydraulic Tomography journal September 2010
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations journal January 2011
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion journal January 2012
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion journal January 2013
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems journal January 2014
Algorithms for Kullback--Leibler Approximation of Probability Measures in Infinite Dimensions journal January 2015
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs: Part I: Deterministic Inversion and Linearized Bayesian Inference journal April 2021
On Bayesian A- and D-Optimal Experimental Designs in Infinite Dimensions journal September 2016
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems journal October 2018
Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources journal January 2014

Similar Records

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate
Journal Article · Mon Jul 13 00:00:00 EDT 2020 · Water Resources Research · OSTI ID:1853043

Related Subjects

54 ENVIRONMENTAL SCIENCES