The ensemble Kalman filter (EnKF) is nowadays recognized as an excellent inverse method for hydraulic
conductivity characterization using transient piezometric head data. and it is proved that the EnKF is
computationally efficient and capable of handling large fields compared to other inverse methods. However,
it is needed a large ensemble size (Chen and Zhang, 2006) to get a high quality estimation, which means a
lots of computation time. Parallel computing is an efficient alterative method to reduce the commutation
time.
Besides, although the EnKF is good accounting for the non linearities of the state equation, it fails when
dealing with non-Gaussian distribution fields. Recently, many methods are developed trying to adapt the
EnKF to non-Gaussian distributions(detailed in the History and present state chapter). Zhou et al. (2011,
2012) have proposed a Normal-Score Ensemble Kalman Filter (NS-EnKF) to character the non-Gaussian
distributed conductivity fields, and already showed that transient piezometric head was enough for hydraulic
conductivity characterization if a training image for the hydraulic conductivity was available. Then in
this work, we will show that, when without such a training image but with enough transient piezometric
head information, the performance of the updated ensemble of realizations in the characterization of the
non-Gaussian reference field.
In the end, we will introduce a new method for parameterizing geostatistical models coupling with the
NS-EnKF in the characterization of a Heterogenous non-Gaussian hydraulic conductivity field.
So, this doctor thesis is mainly including three parts, and the name of the parts as below.
1, Parallelized Ensemble Kalman Filter for Hydraulic Conductivity Characterization.
2, The Power of Transient Piezometric Head Data in Inverse Modeling: An Application of the Localized
Normal-score EnKF with Covariance Inflation in a Heterogenous Bimodal Hydraulic Conductivity Field.
3, Parameterizing geostatistical models coupling with the NS-EnKF for Heterogenous Bimodal Hydraulic
Conductivity characterization. / Xu, T. (2014). Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/43769 / TESIS
| Identifer | oai:union.ndltd.org:upv.es/oai:riunet.upv.es:10251/43769 |
| Date | 03 November 2014 |
| Creators | Xu, Teng |
| Contributors | Gómez Hernández, José Jaime, Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient |
| Publisher | Universitat Politècnica de València |
| Source Sets | Universitat Politècnica de València |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/acceptedVersion |
| Source | Riunet |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, info:eu-repo/semantics/openAccess |
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