Abstract
Uncertainty characterization generally calls for a Monte Carlo analysis of many equally likely realizations that honor both direct information (e.g., conductivity data) and information about the state of the system (e.g., piezometric head or concentration data). The problem faced is how to generate multiple realizations conditioned (to parameter data) and inverse-conditioned (to dependent state data) over a large domain with high resolution. Traditional McMC methods face a big challenge in inverse-conditioning because of its slow convergence. In this study, we comment on several block updating schemes to improve the convergence performance of McMC.
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Acknowledgements
The first author thanks Universidad Politécnica de Valencia for a sabbatical grant during the preparation of this manuscript. The second author also thanks Universidad Politécnica de Valencia for a fellowship that supported him through his doctoral studies. The work on this manuscript also benefited from financial support from the Spanish Ministry of Education and Science through project CGL02004–2008, and from the European Commission through integrated project FI6W-516514.
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Gómez-Hernández, J.J., Fu, J. (2010). Blocking Markov Chain Monte Carlo Schemes for Inverse Stochastic Hydrogeological Modeling. In: Atkinson, P., Lloyd, C. (eds) geoENV VII – Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2322-3_11
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DOI: https://doi.org/10.1007/978-90-481-2322-3_11
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