2015/03/16 by Teng Xu, J. Jaime Gómez‐Hernández · 23 citations
Earth and Planetary Sciences · Engineering · Environmental Science · Mathematics · #Algorithm #Artificial intelligence #Computer science #Covariance #Data assimilation #Ensemble Kalman filter #Extended Kalman filter #Field (mathematics) #Filter (signal processing) #Gaussian #Geology #Geometry #Geophysical and Geoelectrical Methods #Groundwater flow and contamination studies #Hydraulic conductivity #Inverse #Inverse Gaussian distribution #Inverse filter #Kalman filter #Mathematical analysis #Mathematics #Meteorology #Physics #Reservoir Engineering and Simulation Methods #Soil science #Statistics
paper · open access · doi:10.1002/2014wr016320
published in Water Resources Research 51(4), 2227-2242 (Wiley)
openalex publication_date 2015/03/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Abstract 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.