Relevant bibliographies by topics / Iterative ensemble smoother

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Academic literature on the topic 'Iterative ensemble smoother'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Iterative ensemble smoother"

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Raanes, Patrick Nima, Andreas Størksen Stordal, and Geir Evensen. "Revising the stochastic iterative ensemble smoother." Nonlinear Processes in Geophysics 26, no. 3 (2019): 325–38. http://dx.doi.org/10.5194/npg-26-325-2019.

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Abstract. Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For exampl
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Grudzien, Colin, and Marc Bocquet. "A fast, single-iteration ensemble Kalman smoother for sequential data assimilation." Geoscientific Model Development 15, no. 20 (2022): 7641–81. http://dx.doi.org/10.5194/gmd-15-7641-2022.

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Abstract. Ensemble variational methods form the basis of the state of the art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for real-time, short-range forecast systems. We propose a novel estimator in this formalism that is designed for applications in which forecast error dynamics is weakly nonlinear, such as synoptic-scale meteorology. Our method combines the 3D sequential filter analysis and retrospective reanalysis of the classic ensemble Kalman smoother with an iterative ensemble simulation of 4D smoothers. To rigorously derive and contextualize
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Bocquet, M., and P. Sakov. "An iterative ensemble Kalman smoother." Quarterly Journal of the Royal Meteorological Society 140, no. 682 (2013): 1521–35. http://dx.doi.org/10.1002/qj.2236.

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Bocquet, M. "Localization and the iterative ensemble Kalman smoother." Quarterly Journal of the Royal Meteorological Society 142, no. 695 (2016): 1075–89. http://dx.doi.org/10.1002/qj.2711.

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Jiang, Simin, Ruicheng Zhang, Jinbing Liu, Xuemin Xia, Xianwen Li, and Maohui Zheng. "Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation." Water 14, no. 5 (2022): 757. http://dx.doi.org/10.3390/w14050757.

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Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic cond
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Chen, Yan, and Dean S. Oliver. "Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother." Mathematical Geosciences 44, no. 1 (2011): 1–26. http://dx.doi.org/10.1007/s11004-011-9376-z.

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Gineste, Michael, and Jo Eidsvik. "Batch seismic inversion using the iterative ensemble Kalman smoother." Computational Geosciences 25, no. 3 (2021): 1105–21. http://dx.doi.org/10.1007/s10596-021-10043-4.

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AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and eff
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Nezhadali, Mohammad, Tuhin Bhakta, Kristian Fossum, and Trond Mannseth. "Iterative multilevel assimilation of inverted seismic data." Computational Geosciences 26, no. 2 (2022): 241–62. http://dx.doi.org/10.1007/s10596-021-10125-3.

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AbstractIn ensemble-based data assimilation (DA), the ensemble size is usually limited to around one hundred. Straightforward application of ensemble-based DA can therefore result in significant Monte Carlo errors, often manifesting themselves as severe underestimation of parameter uncertainties. Localization is the conventional remedy for this problem. Assimilation of large amounts of simultaneous data enhances the negative effects of Monte Carlo errors. Use of lower-fidelity models reduces the computational cost per ensemble member and therefore renders the possibility to reduce Monte Carlo
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Bocquet, M., and P. Sakov. "Joint state and parameter estimation with an iterative ensemble Kalman smoother." Nonlinear Processes in Geophysics 20, no. 5 (2013): 803–18. http://dx.doi.org/10.5194/npg-20-803-2013.

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Abstract. Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS is a candidate tool for joint state and parameter estimation that may inherit the benefits from both the ensemble filtering and variation
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Zhang, Ruicheng, Nianqing Zhou, Xuemin Xia, Guoxian Zhao, and Simin Jiang. "Joint Estimation of Hydraulic and Biochemical Parameters for Reactive Transport Modelling with a Modified ILUES Algorithm." Water 12, no. 8 (2020): 2161. http://dx.doi.org/10.3390/w12082161.

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Multicomponent reactive transport modeling is a powerful tool for the comprehensive analysis of coupled hydraulic and biochemical processes. The performance of the simulation model depends on the accuracy of related model parameters whose values are usually difficult to determine from direct measurements. In this situation, estimates of these uncertain parameters can be obtained by solving inverse problems. In this study, an efficient data assimilation method, the iterative local updating ensemble smoother (ILUES), is employed for the joint estimation of hydraulic parameters, biochemical param
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Book chapters on the topic "Iterative ensemble smoother"

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Evensen, Geir, Femke C. Vossepoel, and Peter Jan van Leeuwen. "EnRML for History Matching Petroleum Models." In Springer Textbooks in Earth Sciences, Geography and Environment. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96709-3_21.

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AbstractIn this chapter, we present an application of an iterative ensemble smoother for a history-matching case with a reservoir simulator. The application is realistic and represents an actual oil reservoir with production data.
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Conference papers on the topic "Iterative ensemble smoother"

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Trani, M., A. Moncorgé, P. Bergey, and Y. Chen. "Fluid Front History Matching Using an Iterative Ensemble Smoother." In 77th EAGE Conference and Exhibition 2015. EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201413164.

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Gineste, M., J. Eidsvik, and Y. Zheng. "Seismic Waveform Inversion Using an Iterative Ensemble Kalman Smoother." In Second EAGE/PESGB Workshop on Velocities. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201900038.

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Khrulenko, A., A. Shchipanov, and R. Berenblyum. "Conditioning Reservoir Models To Pressure Transients By Iterative Ensemble Smoother." In ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201802133.

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"Estimating rainfall-runoff model parameters using the iterative ensemble smoother." In 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.l1.bennett.

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Luo, X., and P. Ranazzi. "Cross-validation in an Iterative Ensemble Smoother: Stopping Earlier for Better." In ECMOR 2024. European Association of Geoscientists & Engineers, 2024. http://dx.doi.org/10.3997/2214-4609.202437024.

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Gineste, M., and J. Eidsvik. "Seismic Waveform Inversion of Elastic Properties Using an Iterative Ensemble Kalman Smoother." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902267.

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Sankararaj, Rubakumar, Sylvain Ducroux, Dan Kuznetsov, and Fengde Zhou. "Introducing Iterative Ensemble Kalman Smoother to History-Matching Horizontal Coalbed Methane Wells." In Asia Pacific Unconventional Resources Technology Conference 2021. Unconventional Resources Technology Conference, 2021. http://dx.doi.org/10.15530/ap-urtec-2021-208291.

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Chen, Yan, and Dean S. Oliver. "History Matching Of The Norne Full Field Model Using An Iterative Ensemble Smoother." In EAGE Annual Conference & Exhibition incorporating SPE Europec. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/164902-ms.

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Ma, Xiang, Gill Hetz, Xiaochen Wang, Linfeng Bi, Dave Stern, and Nazish Hoda. "A Robust Iterative Ensemble Smoother Method for Efficient History Matching and Uncertainty Quantification." In SPE Reservoir Simulation Conference. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/182693-ms.

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Chen, Y., and D. S. Oliver. "History Matching of the Norne Full Field Model Using an Iterative Ensemble Smoother - (SPE-164902)." In 75th EAGE Conference and Exhibition incorporating SPE EUROPEC 2013. EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20130160.

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