Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Abstract

We combine physics‐based groundwater reactive transport modelling with machine‐learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3H concentrations generated using a high‐fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high‐fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon‐12 (CFC‐12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC‐12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3H observations results in groundwater mean ages that are too large to explain the observed CFC‐12 concentrations. The coupling of the physics‐based reactive transport model with the machine‐learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

“…In recent years, several surrogate model approaches have been suggested to approximate simulation models for uncertainty analysis, such as Polynomial Chaos Expansion (PCE); Gaussian Process Emulation (GPE) [41]; Gaussian processes [42]; Kriging [26,43,44]; Radial Basis Function (RBF) [45]; Support Vector Regression (SVR) [46]; Artificial Neural Network (ANN) [47]; Multi-gene Genetic programming (MGGP) [48]; Kernel Extreme Learning Machine (KELM) [49]; a hybrid approach using the Multilevel Monte Carlo method (MLMC); a graph convolutional neural network and a feed-forward neural network [50]; and a Deep Belief Neural Network (DBNN) [51]. While earlier studies have demonstrated certain achievements, the application of surrogate models encounters challenges related to scalability and accuracy, particularly in scenarios where contaminantrelated associations exhibits pronounced non-linearity or high dimensionality [52].…”

Data science applications in hydrology

“…Using physically‐based models to inform machine learning methods can overcome these limitations and create opportunities for the development of new research frameworks. Thiros et al (2022) use these ideas to solve a classic model parameterization problem. The authors trained a deep learning model with outputs from a computationally‐heavy distributed transport model, in effect generating a machine surrogate.…”