A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework

  • Mohamad E. Gharamti
  • Youssef M. Marzouk
  • Xun Huan
  • Ibrahim Hoteit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8964)


Optimizing 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.


Greedy Algorithm Information Gain Observation Well Forecast Ensemble Greedy Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cavagnaro, D.R., Myung, J.I., Pitt, M.A., Kujala, J.V.: Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science. Neural Comput. 22, 887–905 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Choi, H.-L., How, J.P.: Efficient targeting of sensor networks for large-scale systems. IEEE Trans. Control Syst. Technol. 19, 1569–1577 (2011)CrossRefGoogle Scholar
  3. 3.
    Choi, H-L., How, J.P., Hansen, J.A.: Ensemble-based adaptive targeting of mobile ensor networks. In: Proceedings of the 2007 American Control Conference. ThA09.3, pp. 2393–2398 (2007)Google Scholar
  4. 4.
    Gharamti, M.E., Valstar, J., Hoteit, I.: Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering. Adv. Water Resour. 60, 75–88 (2013)CrossRefGoogle Scholar
  5. 5.
    Gharamti, M.E., Kadoura, A., Valstar, J., Sun, S., Hoteit, I.: Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter. Water Resour. Res. 50, 2444–2467 (2014)CrossRefGoogle Scholar
  6. 6.
    Huan, X., Marzouk, Y.M.: Simulation-based optimal Bayesian experimental design for nonlinear systems. J. Comput. Phys. 232(1), 288–317 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Huan, X., Marzouk, Y.M.: Gradient-based stochastic optimization methods in Bayesian experimental design. Int. J. Uncertainty Quantification 4(6), 479–510 (2014). doi: 10.1615/Int.J.UncertaintQuantification.2014006730 MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Majumdar, S.J., Bishop, C.H., Etherton, B.J., Szunyogh, I., Toth, Z.: Can an ensemble transform Kalman filter predict the reduction in forecast error variance produced by targeted observations? Q. J. Roy. Meteorol. Soc. 126, 1–999 (2000)CrossRefGoogle Scholar
  10. 10.
    Solonen, A., Haario, H., Laine, M.: Simulation-based optimal design using a response variance criterion. J. Comput. Graph. Stat. 21, 234–252 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yakirevich, A., Pachepsky, Y.A., Gish, T.J., Guber, A.K., Kuznetsov, M.Y., Cady, R.E., Nicholson, T.J.: Augmenting of groundwater monitoring networks using information theory and ensemble modeling with pedotransfer functions. J. Hydrol. 501, 13–24 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohamad E. Gharamti
    • 1
    • 4
  • Youssef M. Marzouk
    • 2
  • Xun Huan
    • 2
  • Ibrahim Hoteit
    • 1
    • 3
  1. 1.Earth Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.Department of Aeronautics and AstronauticsMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Applied Mathematics and Computational SciencesKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  4. 4.Mohn-Sverdrup Center, Nansen Environmental and Remote Sensing CenterBergenNorway

Personalised recommendations