Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-351-2019
https://doi.org/10.5194/hess-23-351-2019
Research article
 | 
21 Jan 2019
Research article |  | 21 Jan 2019

Contaminant source localization via Bayesian global optimization

Guillaume Pirot, Tipaluck Krityakierne, David Ginsbourger, and Philippe Renard

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (16 Nov 2017) by Bill X. Hu
AR by Guillaume Pirot on behalf of the Authors (21 Dec 2017)  Manuscript 
ED: Referee Nomination & Report Request started (22 Dec 2017) by Bill X. Hu
RR by Anonymous Referee #3 (16 Jan 2018)
RR by Anonymous Referee #2 (23 Jan 2018)
ED: Reconsider after major revisions (further review by editor and referees) (07 Mar 2018) by Bill X. Hu
AR by Guillaume Pirot on behalf of the Authors (30 May 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (25 Jun 2018) by Bill X. Hu
RR by Anonymous Referee #2 (26 Jul 2018)
RR by Anonymous Referee #3 (31 Jul 2018)
ED: Reconsider after major revisions (further review by editor and referees) (05 Sep 2018) by Bill X. Hu
AR by Guillaume Pirot on behalf of the Authors (17 Oct 2018)  Manuscript 
ED: Referee Nomination & Report Request started (04 Nov 2018) by Bill X. Hu
RR by Anonymous Referee #3 (18 Nov 2018)
RR by Heng Dai (09 Dec 2018)
ED: Publish as is (16 Dec 2018) by Bill X. Hu
AR by Guillaume Pirot on behalf of the Authors (21 Dec 2018)  Manuscript 
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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.