Characterizing curvilinear features using the localized normal-score ensemble Kalman filter

DOI10.1155/2012/805707zbMath1246.76142OpenAlexW2035255936WikidataQ58696836 ScholiaQ58696836MaRDI QIDQ448823

J. Jaime Gómez-Hernández, Liangping Li, Haiyan Zhou

Publication date: 7 September 2012

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/805707

zbMATH Keywords

Kalman filterydraulic conductivity curvilinear patterns

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Flows in porous media; filtration; seepage (76S05)

Related Items (2)

Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification ⋮ Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data

Uses Software

• EnKF
• MODFLOW
• MODFLOW-2000

Cites Work

• Unnamed Item
• Cross-covariances and localization for EnKF in multiphase flow data assimilation
• Sequential Data Assimilation Techniques in Oceanography
• Data Assimilation

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