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    Low-rank Kalman filtering for efficient state estimation of subsurface advective contaminant transport models

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    Type
    Article
    Authors
    El Gharamti, Mohamad cc
    Hoteit, Ibrahim cc
    Sun, Shuyu cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computational Transport Phenomena Lab
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Environmental Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2012-04
    Permanent link to this record
    http://hdl.handle.net/10754/562146
    
    Metadata
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    Abstract
    Accurate knowledge of the movement of contaminants in porous media is essential to track their trajectory and later extract them from the aquifer. A two-dimensional flow model is implemented and then applied on a linear contaminant transport model in the same porous medium. Because of different sources of uncertainties, this coupled model might not be able to accurately track the contaminant state. Incorporating observations through the process of data assimilation can guide the model toward the true trajectory of the system. The Kalman filter (KF), or its nonlinear invariants, can be used to tackle this problem. To overcome the prohibitive computational cost of the KF, the singular evolutive Kalman filter (SEKF) and the singular fixed Kalman filter (SFKF) are used, which are variants of the KF operating with low-rank covariance matrices. Experimental results suggest that under perfect and imperfect model setups, the low-rank filters can provide estimates as accurate as the full KF but at much lower computational effort. Low-rank filters are demonstrated to significantly reduce the computational effort of the KF to almost 3%. © 2012 American Society of Civil Engineers.
    Citation
    El Gharamti, M., Hoteit, I., & Sun, S. (2012). Low-Rank Kalman Filtering for Efficient State Estimation of Subsurface Advective Contaminant Transport Models. Journal of Environmental Engineering, 138(4), 446–457. doi:10.1061/(asce)ee.1943-7870.0000484
    Sponsors
    This publication utilized work supported in part by funds from the KAUST GCR Collaborative Fellow program.
    Publisher
    American Society of Civil Engineers (ASCE)
    Journal
    Journal of Environmental Engineering
    DOI
    10.1061/(ASCE)EE.1943-7870.0000484
    ae974a485f413a2113503eed53cd6c53
    10.1061/(ASCE)EE.1943-7870.0000484
    Scopus Count
    Collections
    Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computational Transport Phenomena Lab

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