A method for enforcing parameter nonnegativity in Bayesian inverse problems with an application to contaminant source identification

By Anna M. Michalak, Peter K. Kitanidis. 2003

109 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/7ed468d4fce545aa427e08c7ed932fde7578e695

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Environmental Engineering

Citations in the corpus

A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields (2010, result): “This case is in line with the studies of [e.g., Hoeksema and Kitanidis, 1984; Michalak and Kitanidis, 2003 ].”
A markov random field model of contamination source identification in porous media flow (2006, methodology): “We refer to these parameters as ‘structure variables’ following the terminology by Michalak and Kitanidis [4].”
Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling (2004, methodology): “Stochastic approaches include geostatistical techniques [Snodgrass and Kitanidis, 1997; Michalak and Kitanidis, 2002, 2003, 2004 ] and the minimum relative entropy method [Woodbury and Ulrych, 1996; Woodbury et al., 1998; Neupauer et al., 2000].”
Impact of sampling volume on the probability density function of steady state concentration (2008, background): “[67] The shape of the concentration pdf is of significance in risk assessment, in the transfer to reactive transport [Cirpka et al., 2008], and in statistical inference [ Michalak and Kitanidis, 2003 ].”
Inverse problems: A Bayesian perspective (2010, background): “…oceanography, hydrology and geophysics may be found in Efendiev et al. (2009), Cui, Fox, Nicholls and O’Sullivan (2010), McKeague, Nicholls, Speer and Herbei (2005), Herbei, McKeague and Speer (2008), McLaughlin and Townley (1996), Michalak and Kitanidis (2003) and Mosegaard and Tarantola (1995).”
Hierarchical Bayesian models for inverse problems in heat conduction (2005, background): “Those include Beck et al. [21] to structural models, Kaipio et al. [16] to electrical impedance tomography, Sabin et al. [22] to grain size prediction, Michalak et al. [ 23 ] to o w in porous media, Osio [24] to engineering design, and Higdon et al. [25] to petroleum engineering.”