High-dimensional nonlinear Bayesian inference of poroelastic fields from pressure data

被引:2
|
作者
Karimi, Mina [1 ]
Massoudi, Mehrdad [2 ]
Dayal, Kaushik [1 ,3 ,4 ,5 ]
Pozzi, Matteo [1 ,5 ]
机构
[1] Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA 15213 USA
[2] Natl Energy Technol Lab, Pittsburgh, PA USA
[3] Carnegie Mellon Univ, Ctr Nonlinear Anal, Dept Math Sci, Pittsburgh, PA 15213 USA
[4] Carnegie Mellon Univ, Dept Mech Engn, Pittsburgh, PA 15213 USA
[5] Carnegie Mellon Univ, Wilton E Scott Inst Energy Innovat, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会; 美国安德鲁·梅隆基金会;
关键词
Hamiltonian Monte Carlo; high-dimensional inference; Markov Chain Monte Carlo; poroelastic model; HAMILTONIAN MONTE-CARLO; STOCHASTIC NEWTON MCMC; INVERSE PROBLEMS; COMPUTATIONAL FRAMEWORK; MODEL-REDUCTION; INJECTION; OKLAHOMA; LANGEVIN;
D O I
10.1177/10812865221140840
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We investigate solution methods for large-scale inverse problems governed by partial differential equations (PDEs) via Bayesian inference. The Bayesian framework provides a statistical setting to infer uncertain parameters from noisy measurements. To quantify posterior uncertainty, we adopt Markov Chain Monte Carlo (MCMC) approaches for generating samples. To increase the efficiency of these approaches in high-dimension, we make use of local information about gradient and Hessian of the target potential, also via Hamiltonian Monte Carlo (HMC). Our target application is inferring the field of soil permeability processing observations of pore pressure, using a nonlinear PDE poromechanics model for predicting pressure from permeability. We compare the performance of different sampling approaches in this and other settings. We also investigate the effect of dimensionality and non-gaussianity of distributions on the performance of different sampling methods.
引用
收藏
页码:2108 / 2131
页数:24
相关论文
共 50 条
  • [21] High-Dimensional Knockoffs Inference for Time Series Data
    Chi, Chien-Ming
    Fan, Yingying
    Ing, Ching-Kang
    Lv, Jinchi
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2025,
  • [22] Bayesian inference for high-dimensional linear regression under mnet priors
    Tan, Aixin
    Huang, Jian
    CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2016, 44 (02): : 180 - 197
  • [23] Bayesian variable selection in clustering high-dimensional data
    Tadesse, MG
    Sha, N
    Vannucci, M
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2005, 100 (470) : 602 - 617
  • [24] Efficient quadratures for high-dimensional Bayesian data assimilation
    Cheng, Ming
    Wang, Peng
    Tartakovsky, Daniel M.
    JOURNAL OF COMPUTATIONAL PHYSICS, 2024, 506
  • [25] Bayesian variable selection for high-dimensional rank data
    Cui, Can
    Singh, Susheela P.
    Staicu, Ana-Maria
    Reich, Brian J.
    ENVIRONMETRICS, 2021, 32 (07)
  • [26] Online Nonlinear Classification for High-Dimensional Data
    Vanli, N. Denizcan
    Ozkan, Huseyin
    Delibalta, Ibrahim
    Kozat, Suleyman S.
    2015 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2015, 2015, : 685 - 688
  • [27] Adaptive Bayesian density regression for high-dimensional data
    Shen, Weining
    Ghosal, Subhashis
    BERNOULLI, 2016, 22 (01) : 396 - 420
  • [28] LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
    Trippe, Brian L.
    Huggins, Jonathan H.
    Agrawal, Raj
    Broderick, Tamara
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
  • [29] Inference of multiple subspaces from high-dimensional data and application to multibody grouping
    Fan, ZM
    Zhou, J
    Wu, Y
    PROCEEDINGS OF THE 2004 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 2, 2004, : 661 - 666
  • [30] On inference in high-dimensional regression
    Battey, Heather S.
    Reid, Nancy
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2023, 85 (01) : 149 - 175