A Variational Bayesian Estimation Scheme For Parametric Point-Like Pollution Source of Groundwater Layers
Type
Conference PaperKAUST Department
Earth Fluid Modeling and Prediction GroupEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2018-09-07Online Publication Date
2018-09-07Print Publication Date
2018-06Permanent link to this record
http://hdl.handle.net/10754/631611
Metadata
Show full item recordAbstract
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.Conference/Event name
20th IEEE Statistical Signal Processing Workshop, SSP 2018Additional Links
https://ieeexplore.ieee.org/document/8450720ae974a485f413a2113503eed53cd6c53
10.1109/SSP.2018.8450720