Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Bayesian inverse modeling techniques are computationally expensive because many forward simulations are needed when sampling the posterior distribution of the parameters. In this paper, we combine the implicit sampling method and generalized polynomial chaos expansion (gPCE) to significantly reduce...

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Bibliographic Details
Published in:Computers & geosciences 2017-11, Vol.108 (C), p.21-32
Main Authors: Liu, Yaning, Pau, George Shu Heng, Finsterle, Stefan
Format: Article
Language:English
Subjects:
Implicit sampling
Inverse modeling
Polynomial chaos expansion
Sparse Bayesian learning
TOUGH2
Vadose zone
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Summary:Bayesian inverse modeling techniques are computationally expensive because many forward simulations are needed when sampling the posterior distribution of the parameters. In this paper, we combine the implicit sampling method and generalized polynomial chaos expansion (gPCE) to significantly reduce the computational cost of performing Bayesian inverse modeling. There are three steps in this approach: (1) find the maximizer of the likelihood function using deterministic approaches; (2) construct a gPCE-based surrogate model using the results from a limited number of forward simulations; and (3) efficiently sample the posterior distribution of the parameters using implicit sampling method. The cost of constructing the gPCE-based surrogate model is further decreased by using sparse Bayesian learning to reduce the number of gPCE coefficients that have to be determined. We demonstrate the approach for a synthetic ponded infiltration experiment simulated with TOUGH2. The surrogate model is highly accurate with mean relative error that is
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2017.04.001