Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-351-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-351-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Contaminant source localization via Bayesian global optimization
Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland
Tipaluck Krityakierne
Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, Thailand
Centre of Excellence in Mathematics, CHE, Bangkok, Thailand
Oeschger Center for Climate Change Research,
University of Bern, Bern, Switzerland
David Ginsbourger
Oeschger Center for Climate Change Research,
University of Bern, Bern, Switzerland
Uncertainty Quantification and Optimal Design group, Idiap Research Institute, Martigny, Switzerland
Institute of Mathematical Statistics and Actuarial Science,
University of Bern, Bern, Switzerland
Philippe Renard
Centre for Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland
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Cited
15 citations as recorded by crossref.
- Contaminant source and aquifer characterization: An application of ES-MDA demonstrating the assimilation of geophysical data Z. Chen et al. 10.1016/j.advwatres.2023.104555
- Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble Kalman filter – Adaptive step length ant colony optimization algorithm Z. Wang et al. 10.1016/j.jhydrol.2021.127352
- A least squares method for identification of unknown groundwater pollution source Z. He et al. 10.2166/nh.2021.088
- Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization S. Saad et al. 10.1016/j.jhydrol.2022.128021
- Lost in Optimization of Water Distribution Systems: Better Call Bayes A. Candelieri et al. 10.3390/w14050800
- Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models R. Scalzo et al. 10.5194/gmd-15-3641-2022
- Fast Calculation of Gaussian Process Multiple-Fold Cross-Validation Residuals and their Covariances D. Ginsbourger & C. Schärer 10.1080/10618600.2024.2353633
- Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al. 10.1080/16000870.2021.1924952
- Bayesian optimization with informative parametric models via sequential Monte Carlo R. Oliveira et al. 10.1017/dce.2022.5
- Using data science to locate nanoparticles in a polymer matrix composite J. Thiem et al. 10.1016/j.compscitech.2021.109205
- A robotic surface inspection framework and machine-learning based optimal segmentation for aerospace and precision manufacturing A. Nandagopal et al. 10.1016/j.jmapro.2024.12.019
- Aggregated GP-based Optimization for Contaminant Source Localization T. Krityakierne & D. Baowan 10.1016/j.orp.2020.100151
- Efficient gHMC Reconstruction of Contaminant Release History D. Barajas-Solano et al. 10.3389/fenvs.2019.00149
- Bayesian optimization of riser configurations J. Elsas et al. 10.1016/j.oceaneng.2021.109402
- Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer V. Todaro et al. 10.1016/j.jhydrol.2021.126215
15 citations as recorded by crossref.
- Contaminant source and aquifer characterization: An application of ES-MDA demonstrating the assimilation of geophysical data Z. Chen et al. 10.1016/j.advwatres.2023.104555
- Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble Kalman filter – Adaptive step length ant colony optimization algorithm Z. Wang et al. 10.1016/j.jhydrol.2021.127352
- A least squares method for identification of unknown groundwater pollution source Z. He et al. 10.2166/nh.2021.088
- Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization S. Saad et al. 10.1016/j.jhydrol.2022.128021
- Lost in Optimization of Water Distribution Systems: Better Call Bayes A. Candelieri et al. 10.3390/w14050800
- Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models R. Scalzo et al. 10.5194/gmd-15-3641-2022
- Fast Calculation of Gaussian Process Multiple-Fold Cross-Validation Residuals and their Covariances D. Ginsbourger & C. Schärer 10.1080/10618600.2024.2353633
- Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al. 10.1080/16000870.2021.1924952
- Bayesian optimization with informative parametric models via sequential Monte Carlo R. Oliveira et al. 10.1017/dce.2022.5
- Using data science to locate nanoparticles in a polymer matrix composite J. Thiem et al. 10.1016/j.compscitech.2021.109205
- A robotic surface inspection framework and machine-learning based optimal segmentation for aerospace and precision manufacturing A. Nandagopal et al. 10.1016/j.jmapro.2024.12.019
- Aggregated GP-based Optimization for Contaminant Source Localization T. Krityakierne & D. Baowan 10.1016/j.orp.2020.100151
- Efficient gHMC Reconstruction of Contaminant Release History D. Barajas-Solano et al. 10.3389/fenvs.2019.00149
- Bayesian optimization of riser configurations J. Elsas et al. 10.1016/j.oceaneng.2021.109402
- Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer V. Todaro et al. 10.1016/j.jhydrol.2021.126215
Discussed (final revised paper)
Latest update: 02 Apr 2025
Short summary
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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