Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates
@article{Elsheikh2014EfficientBI, title={Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates}, author={Ahmed H. Elsheikh and Ibrahim Hoteit and Mary F. Wheeler}, journal={Computer Methods in Applied Mechanics and Engineering}, year={2014}, volume={269}, pages={515-537} }
Figures from this paper
84 Citations
Calibration of channelized subsurface flow models using nested sampling and soft probabilities
- Computer Science
- 2015
Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion
- Computer Science
- 2015
Polynomial surrogates for Bayesian traveltime tomography
- GeologyGEM - International Journal on Geomathematics
- 2021
This paper tackles the issue of the computational load encountered in seismic imaging by Bayesian traveltime inversion. In Bayesian inference, the exploration of the posterior distribution of the…
Improved Nested Sampling and Surrogate‐Enabled Comparison With Other Marginal Likelihood Estimators
- Environmental Science
- 2018
Estimating marginal likelihood is of central importance to Bayesian model selection and/or model averaging. The nested sampling method has been recently used together with the Metropolis‐Hasting…
An evolutionary nested sampling algorithm for Bayesian model updating and model selection using modal measurement
- Computer Science
- 2017
Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific
- Environmental Science
- 2016
AbstractThe authors present a polynomial chaos (PC)–based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model…
A nested sampling particle filter for nonlinear data assimilation
- Computer Science
- 2014
The proposed nested sampling particle filter (NSPF) iteratively builds the posterior distribution by applying a constrained sampling from the prior distribution to obtain particles in high‐likelihood regions of the search space, resulting in a reduction of the number of particles required for an efficient behaviour of particle filters.
Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models
- Computer ScienceJ. Comput. Phys.
- 2019
Probabilistic model updating via variational Bayesian inference and adaptive Gaussian process modeling
- Engineering, Computer Science
- 2021
Accelerating Monte Carlo Markov chains with proxy and error models
- Computer ScienceComput. Geosci.
- 2015
References
SHOWING 1-10 OF 66 REFERENCES
Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems
- Computer ScienceJ. Comput. Phys.
- 2014
An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method
- Mathematics
- 2009
A new approach to modeling inverse problems using a Bayesian inference method is introduced. The Bayesian approach considers the unknown parameters as random variables and seeks the probabilistic…
Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration
- Computer Science
- 2013
This work reports the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems and the results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models.
An iterative stochastic ensemble method for parameter estimation of subsurface flow models
- MathematicsJ. Comput. Phys.
- 2013
Parameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter
- Computer ScienceStochastic Environmental Research and Risk Assessment
- 2012
The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles that is a promising approach for parameter estimation of subsurface flow models.
A stochastic collocation approach to Bayesian inference in inverse problems
- Mathematics
- 2009
We present an efficient numerical strategy for the Bayesian solution of inverse problems. Stochastic collocation methods, based on generalized polynomial chaos (gPC), are used to construct a…
Properties of nested sampling
- Mathematics, Computer Science
- 2010
It is established that nested sampling has an approximation error that vanishes at the standard Monte Carlo rate and that this error is asymptotically Gaussian, and it is shown that the asymPTotic variance of the nested sampling approximation typically grows linearly with the dimension of the parameter.
Bayesian Reservoir History Matching Considering Model and Parameter Uncertainties
- MathematicsMathematical Geosciences
- 2012
This paper presents a consistent Bayesian solution for data integration and history matching for oil reservoirs while accounting for both model and parameter uncertainties. The developed method uses…