NDLI: Contaminant source estimation in a two-layers porous environment using a Bayesian approach

Content Provider	IEEE Xplore Digital Library
Author	Hazart, A. Giovannelli, J.-F. Dubost, S. Chatellier, L.
Copyright Year	2007
Description	Author affiliation: Electricite De France, Chatou (Hazart, A.)
Abstract	Pollution source estimation based on groundwater concentration measurements is an ill-posed inverse problem in that several different sources can supply similar observations. Consequently, constraints on the source must be added. Most of the works in the literature regularize the problem either by introducing parametric source models or by penalizing undesir able solutions. In our work, we use both strategies. In this paper, we focus on point and instantaneous pollution source. Since the source depends on a small number of parameters, the inverse problem becomes over-determined. In addition, the use of prior probability on parameters is a way of dealing with difficult situations in which few observations are available. Information from observations and prior information are combined in a Bayesian framework. The approach leads to a posterior probability given the data that summarizes all the available information about the source. We explore the posterior law with a Markov Chain Monte Carlo sampling method and estimate the source characteristics by posterior mean. The proposed method is applied to a simulated case of pollution.
Starting Page	4757
Ending Page	4760
File Size	332525
Page Count	4
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423923
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Inverse problems Pollution measurement Water pollution Equations Context modeling Research and development Electric variables measurement Measurement errors Parametric statistics
Content Type	Text
Resource Type	Article

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