Accelerating Uncertainty Quantification of Groundwater Flow Modelling Using a Deep Neural Network Proxy

TL;DR

This paper introduces a novel method combining MCMC and deep neural networks to significantly accelerate uncertainty quantification in groundwater flow models, reducing computational costs by up to 50%.

Contribution

It develops a DNN-accelerated MCMC approach with a delayed acceptance hierarchy and on-the-fly bias correction for efficient groundwater model uncertainty analysis.

Findings

Cost reduction of up to 50% in uncertainty quantification.

Effective bias correction of DNN approximation during sampling.

Validated on synthetic 2D and 3D groundwater problems.

Abstract

Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel algorithmic approach which fuses Markov Chain Monte Carlo (MCMC) and Machine Learning methods to accelerate uncertainty quantification for groundwater flow models. We formulate the governing mathematical model as a Bayesian inverse problem, considering model parameters as a random process with an underlying probability distribution. MCMC allows us to sample from this distribution, but it comes with some limitations: it can be prohibitively expensive when dealing with costly likelihood functions, subsequent samples are often highly correlated, and the standard Metropolis-Hastings algorithm suffers from the curse of dimensionality. This paper designs a Metropolis-Hastings proposal which exploits a deep neural network (DNN) approximation of a groundwater flow model, to significantly accelerate MCMC sampling. We modify a delayed acceptance (DA) model hierarchy, whereby proposals are generated by running short subchains using an inexpensive DNN approximation, resulting in a decorrelation of subsequent fine model proposals. Using a simple adaptive error model, we estimate and correct the bias of the DNN approximation with respect to the posterior distribution on-the-fly. The approach is tested on two synthetic examples; a isotropic two-dimensional problem, and an anisotropic three-dimensional problem. The results show that the cost of uncertainty quantification can be reduced by up to 50% compared to single-level MCMC, depending on the precomputation cost and accuracy of the employed DNN.