气候变化领域集成服务门户(CCPortal): Contaminant source localization via Bayesian global optimization

CCPortal

DOI	10.5194/hess-23-351-2019
	Contaminant source localization via Bayesian global optimization
	Pirot G.; Krityakierne T.; Ginsbourger D.; Renard P.
发表日期	2019
ISSN	1027-5606
起始页码	351
结束页码	369
卷号	23 期号:1
英文摘要	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. © Author(s) 2019.
语种	英语
scopus关键词	Budget control; Contamination; Geology; Global optimization; Concentration Measurement; Expected improvements; Gaussian process models; Geological heterogeneities; Global optimization algorithm; Localization problems; Nonlinear objective functions; Prediction uncertainty; Iterative methods; algorithm; Bayesian analysis; connectivity; Gaussian method; hydraulic conductivity; optimization; pollutant source; simulation
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159787
作者单位	Pirot, G., Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland; Krityakierne, T., 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; Ginsbourger, D., 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; Renard, P., Centre for Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland
推荐引用方式 GB/T 7714	Pirot G.,Krityakierne T.,Ginsbourger D.,et al. Contaminant source localization via Bayesian global optimization[J],2019,23(1).
APA	Pirot G.,Krityakierne T.,Ginsbourger D.,&Renard P..(2019).Contaminant source localization via Bayesian global optimization.Hydrology and Earth System Sciences,23(1).
MLA	Pirot G.,et al."Contaminant source localization via Bayesian global optimization".Hydrology and Earth System Sciences 23.1(2019).