Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models

By L. Y. Hu. 2000

290 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6e43fbdf9dec42e8b3c6bd6b1b40338cbd71c1b6

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Citations in the corpus

A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations (2003, methodology): “…et al., 1995; LaVenue et al., 1995], the Markov chain Monte Carlo (MCMC) method [Oliver et al., 1997], and the gradual deformation (GD) method [Hu, 2000], that search for individual realizations of the conductivity field matching available observations rather than inferring estimates for…”
Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data (2003, methodology): “A large number of inverse modelling techniques is available in the literature (e.g. Kitanidis and Vomvoris, 1983; Dagan, 1985; Carrera and Neuman, 1986a,b,c; Sahuquillo et al., 1992; RamaRao et al., 1995; Oliver et al., 1997; Hanna and Yeh, 1998; Hu, 2000).”
Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm (2011, methodology): “The SGSD algorithm is similar to the gradual deformation method [13, 14, 16, 17] in the sense that the updated reservoir model is a linear combination of the current iterative approximate of the reservoir model and an unconditional realization.”
Inverse methods in hydrogeology: Evolution and recent trends (2014, methodology): “(2009) coupled EnKF with Gaussian mix models to handle the nonGaussian conductivity realiza670 tions; Sarma and Chen (2009) developed a kernel EnKF to handle the connectivity of conductivity based 671 on multiple point statistics; Jafarpour and Khodabakhshi (2011) suggested to update the mean of ens”
Bayesian inverse problem and optimization with iterative spatial resampling (2010, methodology): “[17] The gradual deformation method (GDM) [Hu, 2000; Hu et al., 2001] and the probability perturbation method (PPM) [Caers, 2003; Caers and Hoffman, 2006; Johansen et al., 2007] proceed by combining uniformly sampled realizations.”
Geological realism in hydrogeological and geophysical inverse modeling: A review (2015, background): “[35]) such as pilot points [141], self-sequential calibration [56], or gradual deformation [74].”