@inproceedings{00302b86035c4088a4c5341e81ee88ca,
title = "A greedy approach for placement of subsurface aquifer wells in an ensemble filtering framework",
abstract = "Optimizing wells placement may help in better understand-ing 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.",
author = "Gharamti, \{Mohamad E.\} and Marzouk, \{Youssef M.\} and Xun Huan and Ibrahim Hoteit",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014 ; Conference date: 05-11-2014 Through 07-11-2014",
year = "2015",
doi = "10.1007/978-3-319-25138-7\_27",
language = "English",
isbn = "9783319251370",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "301--309",
editor = "Adrian Sandu and Sai Ravela",
booktitle = "Dynamic Data-Driven Environmental Systems Science - 1st International Conference, DyDESS 2014, Revised Selected Papers",
address = "Germany",
}