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    A Variational Bayesian Estimation Scheme For Parametric Point-Like Pollution Source of Groundwater Layers

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    Type
    Conference Paper
    Authors
    Ait-El-Fquih, Boujemaa
    Giovannelli, J. -F.
    Paul, N.
    Girard, A.
    Hoteit, Ibrahim cc
    KAUST Department
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2018-09-07
    Online Publication Date
    2018-09-07
    Print Publication Date
    2018-06
    Permanent link to this record
    http://hdl.handle.net/10754/631611
    
    Metadata
    Show full item record
    Abstract
    This paper considers the identification of point-like source of groundwater pollution. The ill-posed character of this problem has recently led to the introduction of a regularization approach that combines source parametrization, and penalization of undesirable solutions based on prior information about the source parameters, thereby ending up with a parametric Bayesian estimation framework. In this framework, a stochastic-type Markov Chain Monte Carlo (MCMC) method has been introduced as an approximate computation tool of the posterior mean estimate of both source parameters and variance of the (assumed homogeneous) observation noise. Being in the more general case of inhomogeneous noise, our main goal is to propose a deterministic-type computation method based on the variational Bayesian approach. Simulation results suggest that the proposed scheme can provide comparable estimation accuracy to MCMC while requiring less computational time.
    Citation
    Ait-El-Fquih B, Giovannelli J-F, Paul N, Girard A, Hoteit I (2018) A Variational Bayesian Estimation Scheme For Parametric Point-Like Pollution Source of Groundwater Layers. 2018 IEEE Statistical Signal Processing Workshop (SSP). Available: http://dx.doi.org/10.1109/SSP.2018.8450720.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE Statistical Signal Processing Workshop (SSP)
    Conference/Event name
    20th IEEE Statistical Signal Processing Workshop, SSP 2018
    DOI
    10.1109/SSP.2018.8450720
    Additional Links
    https://ieeexplore.ieee.org/document/8450720
    ae974a485f413a2113503eed53cd6c53
    10.1109/SSP.2018.8450720
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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