Localization and regularization for iterative ensemble smoothers

DOI10.1007/s10596-016-9599-7zbMath1387.86042OpenAlexW2547561522MaRDI QIDQ1702330

Yan Chen, Dean S. Oliver

Publication date: 28 February 2018

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-016-9599-7

zbMATH Keywords

regularizationlocalizationlocal analysisensemble Kalman filterhistory matchingiterative ensemble smoother

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32)

Related Items (5)

Efficient derivative-free Bayesian inference for large-scale inverse problems ⋮ Seismic data assimilation with an imperfect model ⋮ Localized ensemble Kalman inversion ⋮ Ensemble Transform Algorithms for Nonlinear Smoothing Problems ⋮ A robust adaptive iterative ensemble smoother scheme for practical history matching applications

Uses Software

• EnKF

Cites Work

• Relation between two common localisation methods for the EnKF
• Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications
• Cross-covariances and localization for EnKF in multiphase flow data assimilation
• Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
• Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• Data Assimilation
• Calculation of the inverse of the covariance

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