Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain

By Wolfgang Nowak, F. P. J. D. Barros, Yoram Rubin. 2010

126 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/8410bc0dc050cda8e3ee0718556b1c120185ae34

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In Mechanical Engineering

Citations in the corpus

A hypothesis‐driven approach to optimize field campaigns (2012, methodology): “…sampling and field campaign strategies can adapt to the interplay between the actual information needs of the goal at hand, the available measurement and investigation techniques, and the specific composition of uncertainty [e.g., Maxwell et al., 1999; de Barros et al., 2009; Nowak et al., 2010].”
A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields (2010, methodology): “The alternative we propose here is to consider the Matérn family of covariance functions [Matérn, 1986], given by [cf. Nowak et al., 2010] c ‘ð Þ ¼ 2 2 1G ð Þ ‘ð Þ B ‘ð Þ; ð8Þ where ‘2 = Pns i¼1 (ri /li) 2, ri, i = 1, .., ns are the components of the lag vector r, li are the corresponding scales,…”
Efficient random walk particle tracking algorithm for advective‐dispersive transport in media with discontinuous dispersion coefficients and water contents (2011, background): “At the same time, inverse modeling studies of multidimensional flow and transport problems, like geostatistical inversions, become more frequent [e.g., Kowalsky et al., 2004; Nowak et al., 2010].”
Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology (2012, background): “This is a clear example of how the response of parameters and corresponding required characterization efforts vary according to the quantity to be predicted, see [7,52].”
Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design (2012, background): “[31] A recent advancement of BMA shifts the problem of discrete model choice to a continuous parameter problem, introducing a continuous spectrum of model alternatives [Nowak et al., 2010].”
Efficient posterior exploration of a high‐dimensional groundwater model from two‐stage Markov chain Monte Carlo simulation and polynomial chaos expansion (2013, background): “…are not very well resolved [e.g., Carrera and Neuman, 1986a, 1986b; Kitanidis, 1986, 1997; Carrera et al., 2005; Cooley and Christensen, 2006; Hernandez et al., 2006; Tonkin and Doherty, 2009; Keating et al., 2010; Troldborg et al., 2010; Nowak et al., 2010; Zeng and Zhang, 2010, and many others].”