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dc.contributor.authorEl Gharamti, Mohamad
dc.contributor.authorMARZOUK, YOUSSEF M.
dc.contributor.authorHuan, Xun
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2017-01-02T08:10:20Z
dc.date.available2017-01-02T08:10:20Z
dc.date.issued2015-11-27
dc.identifier.citationGharamti ME, Marzouk YM, Huan X, Hoteit I (2015) A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework. Lecture Notes in Computer Science: 301–309. Available: http://dx.doi.org/10.1007/978-3-319-25138-7_27.
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.doi10.1007/978-3-319-25138-7_27
dc.identifier.urihttp://hdl.handle.net/10754/622129
dc.description.abstractOptimizing wells placement may help in better understanding subsurface solute transport and detecting contaminant plumes. In this work, we use the ensemble Kalman filter (EnKF) as a data assimilation tool and propose a greedy observational design algorithm to optimally select aquifer wells locations for updating the prior contaminant ensemble. The algorithm is greedy in the sense that it operates sequentially, without taking into account expected future gains. The selection criteria is based on maximizing the information gain that the EnKF carries during the update of the prior uncertainties. We test the efficiency of this algorithm in a synthetic aquifer system where a contaminant plume is set to migrate over a 30 years period across a heterogenous domain.
dc.publisherSpringer Nature
dc.titleA Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalDynamic Data-Driven Environmental Systems Science
dc.conference.date2014-11-05 to 2014-11-07
dc.conference.name1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014
dc.conference.locationCambridge, MA, USA
dc.contributor.institutionMohn-Sverdrup Center, Nansen Environmental and Remote Sensing Center, Bergen, Norway
dc.contributor.institutionDepartment of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, United States
kaust.personEl Gharamti, Mohamad
kaust.personHoteit, Ibrahim
dc.date.published-online2015-11-27
dc.date.published-print2015


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