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Khambhammettu, Prashanth

Nom
Khambhammettu, Prashanth
Affiliation principale
Fonction
Ancien.ne collaborateur.trice
Identifiants
https://libra.unine.ch/handle/123456789/31675
_
0000-0002-8186-5596

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  • Publication
    Accès libre
    The Traveling Pilot Point method. A novel approach to parameterize the inverse problem for categorical fields
    (2020-2)
    Khambhammettu, Prashanth 
    ;
    Renard, Philippe 
    ;
    Doherty, John
    Categorical parameter distributions are common-place in hydrogeological systems consisting of geologic fa cies/categories with distinct properties, e.g., high-permeability channels embedded in a low-permeability ma trix. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. Previous research in this area has been focused on the use of stochastic methods. In this paper, we present a novel approach based on Traveling Pilot points (TRIPS) combined with subspace parameter estimation methods to generate realistic categorical parameter distributions that honor calibration constraints (e.g., - measured water levels). In traditional implementations, aquifer properties (e.g., hydraulic conductivity) are estimated at fixed pilot point locations. In the TRIPS implementation, both the properties associated with the pilot points and their locations are estimated. Tikhonov regularization constraints are incorporated in the param eter estimation process to produce realistic parameter depictions. For a synthetic aquifer system, we solved the categorical inverse problem by combining the TRIPS methodology with two subspace methods: Null Space Monte Carlo (NSMC) and Posterior Covariance (PC). A posterior ensemble developed with the rejection sampling (RS) method is compared against the TRIPS ensembles. The comparisons indicated similarities between the various ensembles and to the reference parameter distribution. Between the two subspace methods, the NSMC method produced an ensemble with more variability than the PC method. These preliminary results suggest that the TRIPS methodology has promise and could be tested on more complicated problems.