Abstract.
Conditioning transmissivity realizations to state variable data is complex due to the non-linear dependence of transmissivity (or any univariate transform of it) and piezometric heads, concentrations or velocities. A review of the literature shows these complexities. The self-calibrating algorithm combines standard geostatistics and non-linear optimization in a way that allows the generation of multiple realizations of logtransmissivity, which are conditioned not only to logtransmissivity measurements but also to piezometric head and concentration data. The self-calibrating method is demonstrated in a two-dimensional synthetic exercise in which the trade-offs between transmissivity, piezometric head and concentration data are analyzed.
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This research was supported by the Spanish Nuclear Waste Management Agency (ENRESA) and Spanish CICYT’s project HID99–0481. Two of the anonymous reviewers are thanked for their constructive comments, which have helped to improve the final version of the manuscript.
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Gómez-Hernández, J., Franssen, HJ. & Sahuquillo, A. Stochastic conditional inverse modeling of subsurface mass transport: A brief review and the self-calibrating method. Stochastic Environmental Research and Risk Assessment 17, 319–328 (2003). https://doi.org/10.1007/s00477-003-0153-5
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DOI: https://doi.org/10.1007/s00477-003-0153-5