A full‐Bayesian approach to the groundwater inverse problem for steady state flow

By Allan D. Woodbury, T. Ulrych. 2000

111 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/844d9743fe4922e083c2aecffe564fd8893f3367

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Citations in the corpus

Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm (2011, methodology): “Probabilistic modeling of the various uncertainties is crucial in formulating the posterior distribution [Woodbury and Ulrych, 2000; Fox et al., 2009; Kaipio and Somersalo, 2004], and various schemes for hydrology models have been developed, such as the Bayesian hierarchical methods of Kavetski et…”
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013, background): “When estimating the posterior probability density function (PPDF) in Bayesian inference, except in special cases in which analytical expressions of the PPDF can be derived [Woodbury and Ulrych, 2000; Hou and Rubin, 2005], the PPDF is usually estimated numerically using sampling techniques.”
Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain (2010, background): “Note that the entire distribution p(s, b, q) has been jointly conditioned on yo [see Kitanidis, 1986; Pardo‐Iguzquiza, 1999; Woodbury and Ulrych, 2000; Diggle and Ribeiro, 2002].”
Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator (2009, background): “[91] A more widespread and more visual test examines scatterplots of the synthetic true field versus the conditional ensemble mean [e.g., Chen and Zhang, 2006; Zhu and Yeh, 2005; Woodbury and Ulrych, 2000] and then computes correlation coefficients.”
A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, background): “Woodbury and Ulrych [137] proposed a full-Bayesian approach to the groundwater flow inverse problem.”
Role of the calibration process in reducing model predictive error (2005, unknown): “If necessary, such an analysis can include geostatistical characterizations of prior parameter uncertainty [see, e.g., Woodbury and Ulrich, 2000; Woodbury and Rubin, 2000, and references therein].”
A generalized grid connectivity–based parameterization for subsurface flow model calibration (2011, methodology): “More recently, model calibration has involved high‐resolution geologic models, with a grid discretization of at least several hundred thousand being not uncommon, where the heterogeneity of hydraulic properties is determined during the data assimilation or model calibration process [Woodbury and Ulr”
Calibration‐constrained Monte Carlo analysis of highly parameterized models using subspace techniques (2009, methodology): “…prior stochastic parameter descriptions through a model [e.g., Kitanidis, 1996; Yeh et al., 1996; Oliver et al., 1997; Kuczera and Parent, 1998; Woodbury and Ulrych, 2000]; the method of stochastic equations [Rubin and Dagan, 1987; Guadagnini and Neuman, 1999; Hernandez et al., 2006];…”
Efficient nonlinear predictive error variance for highly parameterized models (2006, methodology): “, 1994]; calibration-constrained Monte Carlo, Markov-Chain Monte Carlo (MCMC), and other methods that propagate prior stochastic parameter descriptions through the model to develop posterior parameter and prediction probabilities [Kitanidis, 1996; Yeh et al., 1996; Oliver et al., 1997; Kuczera and P”
Aquifer operator scaling and the effect on solute mixing and dispersion (2006, methodology): “…numerical approach uses classical Monte Carlo methods [e.g., Tompson and Gelhar, 1990], and more recently, Bayesian and other conditioning methods [Woodbury and Ulrych, 2000; Feyen et al., 2002;Morse et al., 2003; Huang et al., 2004], to simulate a large number of aquifer realizations (some more…”