Multimodal ensemble Kalman filtering using Gaussian mixture models

DOI10.1007/s10596-010-9205-3zbMath1213.62145OpenAlexW1973853591MaRDI QIDQ536588

Ernesto Della Rossa, Laura Dovera

Publication date: 19 May 2011

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

Full work available at URL: https://doi.org/10.1007/s10596-010-9205-3

zbMATH Keywords

data assimilation

Mathematics Subject Classification ID

Related Items (14)

Mixture ensemble Kalman filters ⋮ Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification ⋮ Evaluation of Gaussian approximations for data assimilation in reservoir models ⋮ Characterization of non-Gaussian geologic facies distribution using ensemble Kalman filter with probability weighted re-sampling ⋮ Conditioning truncated pluri-Gaussian models to facies observations in ensemble-Kalman-based data assimilation ⋮ Calibration of categorical simulations by evolutionary gradual deformation method ⋮ Multimodal ensemble Kalman filtering using Gaussian mixture models ⋮ Performance of the ensemble Kalman filter outside of existing wells for a channelized reservoir ⋮ Towards a hierarchical parametrization to address prior uncertainty in ensemble-based data assimilation ⋮ Facies estimation through data assimilation and structure parameterization ⋮ A residual-driven adaptive Gaussian mixture approximation for Bayesian inverse problems ⋮ Ensemble-based seismic and production data assimilation using selection Kalman model ⋮ Shape and distributed parameter estimation for history matching using a modified Ensemble Kalman filter and level sets ⋮ The Ensemble Kalman Filter for Rare Event Estimation

Uses Software

• EnKF

Cites Work

• Unnamed Item
• Multimodal ensemble Kalman filtering using Gaussian mixture models
• Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
• Stochastic models, estimation, and control. Vol. 1
• Deterministic Nonperiodic Flow
• Note on “Comparison of Model Selection for Regression” by Vladimir Cherkassky and Yunqian Ma
• Nonlinear Bayesian estimation using Gaussian sum approximations
• Data Assimilation

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