25 citations as recorded by crossref.

1. Impact of model and data resolutions in 4D seismic data assimilation applied to an offshore reservoir in Brazil D. Rosa et al. 10.1016/j.petrol.2022.110830
2. Batch seismic inversion using the iterative ensemble Kalman smoother M. Gineste & J. Eidsvik 10.1007/s10596-021-10043-4
3. Marginalized iterative ensemble smoothers for data assimilation A. Stordal et al. 10.1007/s10596-023-10242-1
4. 4D seismic history matching D. Oliver et al. 10.1016/j.petrol.2021.109119
5. On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems F. Parzer & O. Scherzer 10.1007/s00211-022-01314-y
6. Localized ensemble Kalman inversion X. Tong & M. Morzfeld 10.1088/1361-6420/accb08
7. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
8. Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method H. Yan et al. 10.1029/2021WR029610
9. A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage H. Tang et al. 10.1016/j.ijggc.2021.103488
10. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano 10.5194/npg-28-93-2021
11. Subspace Ensemble Randomized Maximum Likelihood with Local Analysis for Time-Lapse-Seismic-Data Assimilation G. Silva Neto et al. 10.2118/205029-PA
12. An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation G. Evensen et al. 10.3934/fods.2021001
13. Sequential multilevel assimilation of inverted seismic data M. Nezhadali et al. 10.1007/s10596-023-10191-9
14. Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems Y. Yeung et al. 10.1016/j.jcp.2024.112788
15. 4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method R. Soares et al. 10.1016/j.petrol.2020.107763
16. Projection of 4D seismic onto the ensemble observation subspace for data assimilation A. Emerick & G. Neto 10.1016/j.geoen.2024.212835
17. A review on optimization algorithms and surrogate models for reservoir automatic history matching Y. Zhao et al. 10.1016/j.geoen.2023.212554
18. An ensemble-based decision workflow for reservoir management Y. Chang & G. Evensen 10.1016/j.petrol.2022.110858
19. Iterative multilevel assimilation of inverted seismic data M. Nezhadali et al. 10.1007/s10596-021-10125-3
20. Investigation on the production data frequency for assimilation with ensemble smoother A. Emerick & G. Neto 10.1016/j.geoen.2023.212356
21. Handling Big Models and Big Data Sets in History-Matching Problems through an Adaptive Local Analysis Scheme R. Soares et al. 10.2118/204221-PA
22. Performance assessment of an iterative ensemble smoother with local analysis to assimilate big 4D seismic datasets applied to a complex pre-salt-like benchmark case C. Maschio et al. 10.1093/jge/gxad099
23. Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning X. Luo et al. 10.1016/j.petrol.2020.107961
24. Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching G. Evensen et al. 10.3389/fams.2019.00047
25. Formulating the history matching problem with consistent error statistics G. Evensen 10.1007/s10596-021-10032-7