Efficient Calibration of Groundwater Contaminant Transport Models Using Bayesian Optimization

Abstract

Numerical modeling is a significant tool to understand the dynamic characteristics of contaminants transport in groundwater. The automatic calibration of highly parametrized and computationally intensive numerical models for the simulation of contaminant transport in the groundwater flow system is a challenging task. While existing methods use general optimization techniques to achieve automatic calibration, the large numbers of numerical model evaluations required in the calibration process lead to high computing overhead and limit the efficiency of model calibration. This paper presents a Bayesian optimization (BO) method for efficient calibration of numerical models of groundwater contaminant transport. A Bayes model is built to fully represent calibration criteria and derive the objective function for model calibration. The efficiency of model calibration is made possible by the probabilistic surrogate model and the expected improvement acquisition function in BO. The probabilistic surrogate model approximates the computationally expensive objective function with a closed-form expression that can be computed efficiently, while the expected improvement acquisition function proposes the most promising model parameters to improve the fitness to the calibration criteria and reduce the uncertainty of the surrogate model. These schemes allow us to find the optimized model parameters effectively by using a small number of numerical model evaluations. Two case studies for the calibration of the Cr(VI) transport model demonstrate that the BO method is effective and efficient in the inversion of hypothetical model parameters, the minimization of the objective function, and the adaptation of different model calibration criteria. Specifically, this promising performance is achieved within 200 numerical model evaluations, which substantially reduces the computing budget for model calibration.

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

“…With advances in computational techniques, numerical simulation of contaminants in ground waters has become a predominant approach for studying contaminants behavior in groundwater systems [12,13]. In recent years, several numerical simulations of groundwater investigations have been conducted to simulate the transport of Cr(VI) contaminant [10,[14][15][16][17].…”

Multitarget Intelligent Recognition of Petrographic Thin Section Images Based on Faster RCNN

“…In recent years, the introduction of big data and artificial intelligence algorithms has been a major development in geological research fields. Machine learning and deep learning algorithms have been used to uncover hidden relationships in massive structured and unstructured geoscience data [1][2][3][4][5][6], identify geochemical anomalies [7][8][9][10][11][12], and predict potential mineral resources [13][14][15][16][17]. Furthermore, with the development of deep learning, image-based computer vision technology has made it possible to intelligently identify and classify rocks by their optical properties [18][19][20][21][22].…”