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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 23, issue 1
Hydrol. Earth Syst. Sci., 23, 351-369, 2019
https://doi.org/10.5194/hess-23-351-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 351-369, 2019
https://doi.org/10.5194/hess-23-351-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Jan 2019

Research article | 21 Jan 2019

Contaminant source localization via Bayesian global optimization

Guillaume Pirot1, Tipaluck Krityakierne2,3,4, David Ginsbourger4,5,6, and Philippe Renard7 Guillaume Pirot et al.
  • 1Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland
  • 2Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, Thailand
  • 3Centre of Excellence in Mathematics, CHE, Bangkok, Thailand
  • 4Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
  • 5Uncertainty Quantification and Optimal Design group, Idiap Research Institute, Martigny, Switzerland
  • 6Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
  • 7Centre for Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland

Abstract. Contaminant source localization problems require efficient and robust methods that can account for geological heterogeneities and accommodate relatively small data sets of noisy observations. As realism commands hi-fidelity simulations, computation costs call for global optimization algorithms under parsimonious evaluation budgets. Bayesian optimization approaches are well adapted to such settings as they allow the exploration of parameter spaces in a principled way so as to iteratively locate the point(s) of global optimum while maintaining an approximation of the objective function with an instrumental quantification of prediction uncertainty. Here, we adapt a Bayesian optimization approach to localize a contaminant source in a discretized spatial domain. We thus demonstrate the potential of such a method for hydrogeological applications and also provide test cases for the optimization community. The localization problem is illustrated for cases where the geology is assumed to be perfectly known. Two 2-D synthetic cases that display sharp hydraulic conductivity contrasts and specific connectivity patterns are investigated. These cases generate highly nonlinear objective functions that present multiple local minima. A derivative-free global optimization algorithm relying on a Gaussian process model and on the expected improvement criterion is used to efficiently localize the point of minimum of the objective functions, which corresponds to the contaminant source location. Even though concentration measurements contain a significant level of proportional noise, the algorithm efficiently localizes the contaminant source location. The variations of the objective function are essentially driven by the geology, followed by the design of the monitoring well network. The data and scripts used to generate objective functions are shared to favor reproducible research. This contribution is important because the functions present multiple local minima and are inspired from a practical field application. Sharing these complex objective functions provides a source of test cases for global optimization benchmarks and should help with designing new and efficient methods to solve this type of problem.

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To localize the source of a contaminant in the subsurface, based on concentration observations at some wells, we propose to test different possible locations and minimize the misfit between observed and simulated concentrations. We use a global optimization technique that relies on an expected improvement criterion, which allows a good exploration of the parameter space, avoids the trapping of local minima and quickly localizes the source of the contaminant on the presented synthetic cases.
To localize the source of a contaminant in the subsurface, based on concentration observations...
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