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Inverse sequential simulation: A new approach for the characterization of hydraulic conductivities demonstrated on a non‐ G aussian field
Author(s) -
Xu Teng,
GómezHernández J. Jaime
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr016320
Subject(s) - ensemble kalman filter , inverse , hydraulic conductivity , kalman filter , gaussian , covariance , extended kalman filter , algorithm , inverse gaussian distribution , field (mathematics) , filter (signal processing) , inverse filter , data assimilation , computer science , mathematics , geology , meteorology , soil science , physics , artificial intelligence , mathematical analysis , statistics , geometry , distribution (mathematics) , quantum mechanics , pure mathematics , computer vision , soil water
Inverse sequential simulation (iSS) is a new inverse modeling approach for the characterization of hydraulic conductivity fields based on sequential simulation. It is described and demonstrated in a synthetic aquifer with non‐Gaussian spatial features, and compared against the normal‐score ensemble Kalman filter (NS‐EnKF). The new approach uses the sequential simulation paradigm to generate realizations borrowing from the ensemble Kalman filter the idea of using the experimental nonstationary cross‐covariance between conductivities and piezometric heads computed on an ensemble of realizations. The resulting approach is fully capable of retrieving the non‐Gaussian patterns of the reference field after conditioning on the piezometric heads with results comparable of those obtained by the NS‐EnKF.