Novel iterative ensemble smoothers derived from a class of generalized cost functions

DOI10.1007/s10596-021-10046-1zbMath1460.86039OpenAlexW3143044750MaRDI QIDQ2027194

Xiao-Dong Luo

Publication date: 25 May 2021

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

Full work available at URL: https://doi.org/10.1007/s10596-021-10046-1

zbMATH Keywords

inverse problemdata assimilationhistory matchinggeneralized iterative ensemble smoothersmixture of \(\ell_p\) norms

Mathematics Subject Classification ID

Inverse problems in geophysics (86A22)

Related Items (2)

Ensemble Kalman method for learning turbulence models from indirect observation data ⋮ Data assimilation with soft constraints (DASC) through a generalized iterative ensemble smoother

Uses Software

• EnKF

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• Iterative regularization for ensemble data assimilation in reservoir models
• Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
• Analysis of iterative ensemble smoothers for solving inverse problems
• Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data
• Conditional simulation of complex geological structures using multiple-point statistics
• A robust adaptive iterative ensemble smoother scheme for practical history matching applications
• The Bayesian elastic net
• Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization
• The Bayesian Lasso
• Advanced Time Series Data Analysis
• Inverse Problem Theory and Methods for Model Parameter Estimation
• The ensemble Kalman filter for combined state and parameter estimation

This page was built for publication: Novel iterative ensemble smoothers derived from a class of generalized cost functions