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
  • [41] High-Dimensional Bayesian Geostatistics
    Banerjee, Sudipto
    BAYESIAN ANALYSIS, 2017, 12 (02): : 583 - 614
  • [42] Bayesian Variable Selection in Clustering High-Dimensional Data With Substructure
    Swartz, Michael D.
    Mo, Qianxing
    Murphy, Mary E.
    Lupton, Joanne R.
    Turner, Nancy D.
    Hong, Mee Young
    Vannucci, Marina
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2008, 13 (04) : 407 - 423
  • [43] Hierarchical Bayesian Modeling of Mediation by High-Dimensional Omics Data
    Thomas, Duncan
    GENETIC EPIDEMIOLOGY, 2016, 40 (07) : 619 - 619
  • [44] Bayesian regression based on principal components for high-dimensional data
    Lee, Jaeyong
    Oh, Hee-Seok
    JOURNAL OF MULTIVARIATE ANALYSIS, 2013, 117 : 175 - 192
  • [45] Scalable Bayesian variable selection for structured high-dimensional data
    Chang, Changgee
    Kundu, Suprateek
    Long, Qi
    BIOMETRICS, 2018, 74 (04) : 1372 - 1382
  • [46] Far from Asymptopia: Unbiased High-Dimensional Inference Cannot Assume Unlimited Data
    Abbott, Michael C.
    Machta, Benjamin B.
    ENTROPY, 2023, 25 (03)
  • [47] Bayesian Function-on-Scalars Regression for High-Dimensional Data
    Kowal, Daniel R.
    Bourgeois, Daniel C.
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2020, 29 (03) : 629 - 638
  • [48] Bayesian variable selection in clustering high-dimensional data with substructure
    Michael D. Swartz
    Qianxing Mo
    Mary E. Murphy
    Joanne R. Lupton
    Nancy D. Turner
    Mee Young Hong
    Marina Vannucci
    Journal of Agricultural, Biological, and Environmental Statistics, 2008, 13 : 407 - 423
  • [49] Sparse Bayesian variable selection for classifying high-dimensional data
    Yang, Aijun
    Lian, Heng
    Jiang, Xuejun
    Liu, Pengfei
    STATISTICS AND ITS INTERFACE, 2018, 11 (02) : 385 - 395
  • [50] Local Bayesian network structure learning for high-dimensional data
    Wang, Yangyang
    Gao, Xiaoguang
    Ru, Xinxin
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2024, 46 (08): : 2676 - 2685