Inverse sequential simulation: Performance and implementation details |
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| Institution: | 1. Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. Da Vinci 32, 20133 Milano, Italy;2. Università della Calabria, Dipartimento di Ingegneria per l''Ambiente e il Territorio e Ingegneria Chimica, via P Bucci 42B, 87036 Rende (CS), Italy;3. University of Arizona, Department of Hydrology and Water Resources, Tucson, AZ, USA;1. S.S. Papadopulos & Associates, Inc., 7944 Wisconsin Avenue, Bethesda, MD 20814, USA;2. Quantitative Decisions, 1235 Wendover Road, Suite 100, Rosemont, PA 19010, USA;3. E-PUR, LLC, World Trade Center, 121 SW Salmon Street, Suite 900, Portland, OR 97204, USA;1. Department of Civil and Environmental Engineering, Western University, London, Ontario N6A 5B9, Canada;2. Department of Coastal and Marine Systems Science, Coastal Carolina University, Conway, SC 25926, USA;3. Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA;1. Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza 64, 01-452 Warsaw, Poland;2. Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland |
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| Abstract: | For good groundwater flow and solute transport numerical modeling, it is important to characterize the formation properties. In this paper, we analyze the performance and important implementation details of a new approach for stochastic inverse modeling called inverse sequential simulation (iSS). This approach is capable of characterizing conductivity fields with heterogeneity patterns difficult to capture by standard multiGaussian-based inverse approaches. The method is based on the multivariate sequential simulation principle, but the covariances and cross-covariances used to compute the local conditional probability distributions are computed by simple co-kriging which are derived from an ensemble of conductivity and piezometric head fields, in a similar manner as the experimental covariances are computed in an ensemble Kalman filtering. A sensitivity analysis is performed on a synthetic aquifer regarding the number of members of the ensemble of realizations, the number of conditioning data, the number of piezometers at which piezometric heads are observed, and the number of nodes retained within the search neighborhood at the moment of computing the local conditional probabilities. The results show the importance of having a sufficiently large number of all of the mentioned parameters for the algorithm to characterize properly hydraulic conductivity fields with clear non-multiGaussian features. |
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