Transport in heterogeneous sediments with multimodal conductivity and hierarchical organization across scales

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Representing aquifer architecture in macrodispersivity models with an analytical solution of the transition probability matrix (2007, methodology): “In the Markov chain model, both conceptual facies model and quantitative information (proportions, lengths, juxtapositioning frequency) can be combined to mathematically represent facies architecture [Ritzi, 2000; Zhang, 2002; Rubin, 2003; Ritzi et al., 2004; Dai et al., 2004a, 2005 ].”
A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields (2010, background): “It could also be a hybrid approach of these two concepts, whereby the model domain is subdivided into geological units, with each of the geological units characterized by a different geostatistical model [cf. Rubin, 1995; Dai et al., 2004; Ritzi et al., 2004; Rubin et al., 2006].”
Why did Sudicky [1986] find an exponential‐like spatial correlation structure for hydraulic conductivity at the Borden research site? (2006, methodology): “, 2004; Dai et al., 2005] have been consistent with the results presented here for the Borden site in that the p̂ro(h)̂tro,ji(h) terms were found to mostly define the shape and range of the hydraulic conductivity correlation structure. However, many wellstudied examples are needed in order to unders”