Improving the ensemble estimate of the Kalman gain by bootstrap sampling

DOI10.1007/s11004-010-9267-8zbMath1184.86017OpenAlexW2115322479MaRDI QIDQ964859

Dean S. Oliver, Yan-Fen Zhang

Publication date: 21 April 2010

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

Full work available at URL: https://doi.org/10.1007/s11004-010-9267-8

zbMATH Keywords

bootstrapEnKF-SKehierarchical filterscreened Kalman gainspurious correlations

Mathematics Subject Classification ID

Bootstrap, jackknife and other resampling methods (62F40) Geostatistics (86A32)

Related Items (9)

Ensemble-based multi-scale history-matching using second-generation wavelet transform ⋮ Deep learning-aided image-oriented history matching of geophysical data ⋮ Data-space inversion with ensemble smoother ⋮ Recent progress on reservoir history matching: a review ⋮ Filtering with state space localized Kalman gain ⋮ Distributed parameter and state estimation in petroleum reservoirs ⋮ Evaluation and error analysis: Kalman gain regularization versus covariance regularization ⋮ Using a machine learning proxy for localization in ensemble data assimilation ⋮ Towards a hierarchical parametrization to address prior uncertainty in ensemble-based data assimilation

Uses Software

• EnKF
• bootstrap

Cites Work

• 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
• Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter
• Unnamed Item

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