September  2021, 3(3): 331-369. doi: 10.3934/fods.2021011

Iterative ensemble Kalman methods: A unified perspective with some new variants

1. 

Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia

2. 

Department of Statistics, University of Chicago, Chicago, IL 60637, USA

* Corresponding author: Daniel Sanz-Alonso

Received  October 2020 Revised  January 2021 Published  September 2021 Early access  April 2021

Fund Project: NKC is supported by KAUST baseline funding. DSA is thankful for the support of NSF and NGA through the grant DMS-2027056. The work of DSA was also partially supported by the NSF Grant DMS-1912818/1912802

This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We identify, compare and develop three subfamilies of ensemble methods that differ in the objective they seek to minimize and the derivative-based optimization scheme they approximate through the ensemble. Our work emphasizes two principles for the derivation and analysis of iterative ensemble Kalman methods: statistical linearization and continuum limits. Following these guiding principles, we introduce new iterative ensemble Kalman methods that show promising numerical performance in Bayesian inverse problems, data assimilation and machine learning tasks.

Citation: Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso. Iterative ensemble Kalman methods: A unified perspective with some new variants. Foundations of Data Science, 2021, 3 (3) : 331-369. doi: 10.3934/fods.2021011
References:
[1]

S. I. AanonsenG. NævdalD. S. OliverA. C. Reynolds and B. Vallès, The ensemble Kalman filter in reservoir engineering-A review, SPE J., 14 (2009), 393-412.  doi: 10.2118/117274-PA.  Google Scholar

[2]

D. J. Albers, P.-A. Blancquart, M. E. Levine, E. Esmaeilzadeh Seylabi and A. Stuart, Ensemble Kalman methods with constraints, Inverse Problems, 35 (2019), 28pp. doi: 10.1088/1361-6420/ab1c09.  Google Scholar

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J. L. Anderson, An ensemble adjustment Kalman filter for data assimilation, Monthly Weather Review, 129 (2001), 2884-2903.  doi: 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.  Google Scholar

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D. BlömkerC. Schillings and P. Wacker, A strongly convergent numerical scheme from ensemble Kalman inversion, SIAM J. Numer. Anal., 56 (2018), 2537-2562.  doi: 10.1137/17M1132367.  Google Scholar

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N. K. Chada, M. A. Iglesias, L. Roininen and A. M. Stuart, Parameterizations for ensemble Kalman inversion, Inverse Problems, 34 (2018), 31pp. doi: 10.1088/1361-6420/aab6d9.  Google Scholar

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N. K. ChadaC. Schillings and S. Weissmann, On the incorporation of box-constraints for ensemble Kalman inversion, Foundations of Data Science, 1 (2019), 433-456.  doi: 10.3934/fods.2019018.  Google Scholar

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N. K. ChadaA. M. Stuart and X. T. Tong, Tikhonov regularization within ensemble Kalman inversion, SIAM J. Numer. Anal., 58 (2020), 1263-1294.  doi: 10.1137/19M1242331.  Google Scholar

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Y. Chen and D. S. Oliver, Ensemble randomized maximum likelihood method as an iterative ensemble smoother, Mathematical Geosciences, 44 (2012), 1-26.  doi: 10.1007/s11004-011-9376-z.  Google Scholar

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show all references

References:
[1]

S. I. AanonsenG. NævdalD. S. OliverA. C. Reynolds and B. Vallès, The ensemble Kalman filter in reservoir engineering-A review, SPE J., 14 (2009), 393-412.  doi: 10.2118/117274-PA.  Google Scholar

[2]

D. J. Albers, P.-A. Blancquart, M. E. Levine, E. Esmaeilzadeh Seylabi and A. Stuart, Ensemble Kalman methods with constraints, Inverse Problems, 35 (2019), 28pp. doi: 10.1088/1361-6420/ab1c09.  Google Scholar

[3]

B. D. O. Anderson and J. B. Moore, Optimal Filtering, Information and System Sciences Series, (1979). Google Scholar

[4]

J. L. Anderson, An ensemble adjustment Kalman filter for data assimilation, Monthly Weather Review, 129 (2001), 2884-2903.  doi: 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.  Google Scholar

[5]

B. M. Bell, The iterated Kalman smoother as a Gauss-Newton method, SIAM J. Optim., 4 (1994), 626-636.  doi: 10.1137/0804035.  Google Scholar

[6]

B. M. Bell and F. W. Cathey, The iterated Kalman filter update as a Gauss-Newton method, IEEE Trans. Automat. Control, 38 (1993), 294-297.  doi: 10.1109/9.250476.  Google Scholar

[7]

D. P. Bertsakas, Incremental least squares method and the extended Kalman filter, SIAM J. Optim, 6 (1996), 807-822.  doi: 10.1137/S1052623494268522.  Google Scholar

[8]

D. BlömkerC. Schillings and P. Wacker, A strongly convergent numerical scheme from ensemble Kalman inversion, SIAM J. Numer. Anal., 56 (2018), 2537-2562.  doi: 10.1137/17M1132367.  Google Scholar

[9]

D. Blömker, C. Schillings, P. Wacker and S. Weissmann, Well posedness and convergence analysis of the ensemble Kalman inversion, Inverse Problems, 35 (2019), 32pp. doi: 10.1088/1361-6420/ab149c.  Google Scholar

[10]

N. K. Chada, Analysis of hierarchical ensemble Kalman inversion, preprint, arXiv: 1801.00847. Google Scholar

[11]

N. K. Chada, M. A. Iglesias, L. Roininen and A. M. Stuart, Parameterizations for ensemble Kalman inversion, Inverse Problems, 34 (2018), 31pp. doi: 10.1088/1361-6420/aab6d9.  Google Scholar

[12]

N. K. ChadaC. Schillings and S. Weissmann, On the incorporation of box-constraints for ensemble Kalman inversion, Foundations of Data Science, 1 (2019), 433-456.  doi: 10.3934/fods.2019018.  Google Scholar

[13]

N. K. ChadaA. M. Stuart and X. T. Tong, Tikhonov regularization within ensemble Kalman inversion, SIAM J. Numer. Anal., 58 (2020), 1263-1294.  doi: 10.1137/19M1242331.  Google Scholar

[14]

N. K. Chada and X. T. Tong, Convergence acceleration of ensemble Kalman inversion in nonlinear settings, preprint, arXiv: 1911.02424. Google Scholar

[15]

Y. Chen and D. S. Oliver, Ensemble randomized maximum likelihood method as an iterative ensemble smoother, Mathematical Geosciences, 44 (2012), 1-26.  doi: 10.1007/s11004-011-9376-z.  Google Scholar

[16]

J. E. Dennis Jr. and R. B. Schnabel, Numerical methods for unconstrained optimization and nonlinear equations, Classics in Applied Mathematics, 16, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1996. doi: 10.1137/1.9781611971200.  Google Scholar

[17]

Z. Ding and Q. Li, Ensemble Kalman sampler: Mean-field limit and convergence analysis, SIAM J. Math. Anal., 53 (2021), 1546-1578.  doi: 10.1137/20M1339507.  Google Scholar

[18]

A. A. Emerick and A. C. Reynolds, Ensemble smoother with multiple data assimilation, Computers & Geosciences, 55 (2013), 3-15.  doi: 10.1016/j.cageo.2012.03.011.  Google Scholar

[19]

O. G. ErnstB. Sprungk and H.-J. Starkloff, Analysis of the ensemble and polynomial chaos Kalman filters in Bayesian inverse problems, SIAM/ASA J. Uncertain. Quantif., 3 (2015), 823-851.  doi: 10.1137/140981319.  Google Scholar

[20]

G. Evensen, Data Assimilation. The Ensemble Kalman Filter, Springer-Verlag, Berlin, 2009. doi: 10.1007/978-3-642-03711-5.  Google Scholar

[21]

G. Evensen and P. J. Van Leeuwen, Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model, Monthly Weather Review, 124 (1996), 85-96.  doi: 10.1175/1520-0493(1996)124<0085:AOGADF>2.0.CO;2.  Google Scholar

[22]

A. Garbuno-InigoF. HoffmannW. Li and A. M. Stuart, Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler, SIAM J. Appl. Dyn. Syst., 19 (2020), 412-441.  doi: 10.1137/19M1251655.  Google Scholar

[23]

I. Grooms, A note on the formulation of the Ensemble Adjustment Kalman Filter, preprint, arXiv: 2006.02941. Google Scholar

[24]

Y. Gu and D. S. Oliver, An iterative ensemble Kalman filter for multiphase fluid flow data assimilation, Spe Journal, 12 (2007), 438-446.  doi: 10.2118/108438-PA.  Google Scholar

[25]

P. A. Guth, C. Schillings and S. Weissmann, Ensemble Kalman filter for neural network based one-shot inversion, preprint, arXiv: 2005.02039. Google Scholar

[26]

E. Haber, F. Lucka and L. Ruthotto, Never look back - A modified EnKF method and its application to the training of neural networks without back propagation, preprint, arXiv: 1805.08034. Google Scholar

[27]

M. Hanke, A regularizing Levenberg-Marquardt scheme, with applications to inverse groundwater filtration problems, Inverse Problems, 13 (1997), 79-95.  doi: 10.1088/0266-5611/13/1/007.  Google Scholar

[28]

M. Herty and G. Visconti, Kinetic methods for inverse problems, Kinet. Relat. Models, 12 (2019), 1109-1130.  doi: 10.3934/krm.2019042.  Google Scholar

[29]

M. A. Iglesias, A regularising iterative ensemble Kalman method for PDE-constrained inverse problems, Inverse Problems, 32 (2016), 45pp. doi: 10.1088/0266-5611/32/2/025002.  Google Scholar

[30]

M. A. Iglesias, K. J. H. Law and A. M. Stuart, Ensemble Kalman methods for inverse problems, Inverse Problems, 29 (2013), 20pp. doi: 10.1088/0266-5611/29/4/045001.  Google Scholar

[31]

M. A. IglesiasK. LinS. Lu and A. M. Stuart, Filter based methods for statistical linear inverse problems, Commun. Math. Sci., 15 (2017), 1867-1895.  doi: 10.4310/CMS.2017.v15.n7.a4.  Google Scholar

[32]

M. A. Iglesias and Y. Yang, Adaptive regularisation for ensemble Kalman inversion, Inverse Problems, 37 (2021), 40pp. doi: 10.1088/1361-6420/abd29b.  Google Scholar

[33]

A. H. Jazwinski, Stochastic Processes and Filtering Theory, Courier Corporation, 2007. Google Scholar

[34]

R. E. Kalman, A new approach to linear filtering and prediction problems, Trans. ASME Ser. D. J. Basic Engrg., 82 (1960), 35-45.  doi: 10.1115/1.3662552.  Google Scholar

[35]

E. KalnayS. K. ParkZ.-X. Pu and J. Gao, Application of the quasi-inverse method to data assimilation, Monthly Weather Review, 128 (2000), 864-875.  doi: 10.1175/1520-0493(2000)128<0864:AOTQIM>2.0.CO;2.  Google Scholar

[36]

B. Katltenbacher, A. Neubauer and O. Scherzer, Iterative Regularization Methods for Nonlinear Ill-Posed Problems, Radon Series on Computational and Applied Mathematics, 6, Walter de Gruyter GmbH & Co. KG, Berlin, 2008. doi: 10.1515/9783110208276.  Google Scholar

[37]

D. T. B. KellyK. J. H. Law and A. M. Stuart, Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time, Nonlinearity, 27 (2014), 2579-5604.  doi: 10.1088/0951-7715/27/10/2579.  Google Scholar

[38]

N. B. Kovachki and A. M. Stuart, Ensemble Kalman inversion: A derivative-free technique for machine learning tasks, Inverse Problems, 35 (2019), 35pp. doi: 10.1088/1361-6420/ab1c3a.  Google Scholar

[39]

W. G. Lawson and J. A. Hansen, Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth, Monthly Weather Review, 132 (2004), 1966-1981.  doi: 10.1175/1520-0493(2004)132<1966:IOSADF>2.0.CO;2.  Google Scholar

[40]

Y. Lee, $l_p$ regularization for ensemble Kalman inversion, preprint, arXiv: 2009.03470. Google Scholar

[41]

G. Li and A. C. Reynolds, An iterative ensemble Kalman filter for data assimilation, SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, Anaheim, CA, 2007. doi: 10.2118/109808-MS.  Google Scholar

[42]

A. C. Lorenc, Analysis methods for numerical weather prediction, Quart. J. Roy. Meteor. Soc., 112 (1986), 1177-1194.  doi: 10.1002/qj.49711247414.  Google Scholar

[43]

R. J. Lorentzen, K.-K. Fjelde, J. Froyen, A. C. V. M. Lage, G. Nævdal and E. H. Vefring, Underbalanced drilling: Real time data interpretation and decision support, SPE/IADC Drilling Conference, Amsterdam, Netherlands, 2001. doi: 10.2118/67693-MS.  Google Scholar

[44]

S. Lu and S. V. Pereverzev, Multi-parameter regularization and its numerical realization, Numer. Math., 118 (2001), 1-31.  doi: 10.1007/s00211-010-0318-3.  Google Scholar

[45]

A. J. Madja and J. Harlim, Filtering Complex Turbulent Systems, Cambridge University Press, Cambridge, 2012. doi: 10.1017/CBO9781139061308.  Google Scholar

[46]

J. Mandel, E. Bergou and S. Gratton, 4DVAR by ensemble Kalman smoother, preprint, arXiv: 1304.5271. Google Scholar

[47]

G. Nævdal, T. Mannseth and E. H Vefring, Instrumented wells and near-well reservoir monitoring through ensemble Kalman filter, Proceedings of 8th European Conference on the Mathematics of Oil Recovery, Freiberg, Germany, 2001. Google Scholar

[48]

A. S. Nemirovsky and D. B. Yudin, Problem Complexity and Method Efficiency in Optimization, Wiley-Interscience Series in Discrete Mathematics, John Wiley & Sons, Inc., New York, 1983.  Google Scholar

[49]

J. Nocedal and S. J. Wright, Numerical Optimization, Springer Series in Operations Research and Financial Engineering, Springer, New York, 2006. doi: 10.1007/978-0-387-40065-5.  Google Scholar

[50]

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Figure 1.  Ensemble members (green) after $ 100 $ iterations, with truth $ u^\dagger $ (red star) and contour plot of (unnormalized) posterior density
Figure 2.  Evolution of the Frobenius norm of the ensemble covariance $ P^{uu}(t) $. For reference, we also plot the Frobenius norm of the true posterior covariance (red dashed line). The norm of IEKF-RZL blows up after a few iterations. The norms of the EKI and TEKI are almost identical and monotonically decreasing. The norms of the new variants EKI-SL and IEKF-SL are similar and stabilize after around 40 iterations. The norm of IEKF lies between those of the old and new variants
Figure 3.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $
Figure 4.  TEKI, IEKF & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $
Figure 5.  Ensemble mean (red) at the final iteration, with 10, 90-quantiles (blue)
Figure 6.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $
Figure 7.  IEKF, TEKI & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $
Figure 8.  Ensemble mean (red) at the final iteration, with 10, 90-quantiles (blue)
Figure 9.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $
Figure 10.  IEKF, TEKI & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $
Figure 11.  Tikhonov-Phillips objective function with respect to two randomly chosen coordinates
Figure 12.  Ensemble mean (red) at the final iteration, with 10, 90-quantiles (blue)
Figure 13.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $
Figure 14.  IEKF, TEKI & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $
Table 1.  Roadmap to the algorithms considered in this paper. We use the abbreviations GN and LM for Gauss-Newton and Levenberg-Marquardt. The numbers in parenthesis represent the subsection in which each algorithm is introduced
Objective Optimization Derivative Method Ensemble Method New Variant
$ {\mathtt{J}}_{\text{TP}} $ GN IExKF (2.1) IEKF (3.1) IEKF-SL (4.1)
$ {\mathtt{J}}_{\text{DM}} $ LM LM-DM (2.2) EKI (3.2) EKI-SL (4.2)
$ {\mathtt{J}}_{\text{TP}} $ LM LM-TP (2.3) TEKI (3.3) TEKI-SL (4.3)
Objective Optimization Derivative Method Ensemble Method New Variant
$ {\mathtt{J}}_{\text{TP}} $ GN IExKF (2.1) IEKF (3.1) IEKF-SL (4.1)
$ {\mathtt{J}}_{\text{DM}} $ LM LM-DM (2.2) EKI (3.2) EKI-SL (4.2)
$ {\mathtt{J}}_{\text{TP}} $ LM LM-TP (2.3) TEKI (3.3) TEKI-SL (4.3)
Table 2.  Summary of the main algorithms in Sections 2 and 3
Objective Optimization Derivative Method Ensemble Method
$ {\mathtt{J}}_{\text{TP}} $ GN IExKF IEKF
$ {\mathtt{J}}_{\text{DM}} $ LM ILM-DM EKI
$ {\mathtt{J}}_{\text{TP}} $ LM ILM-TP TEKI
Objective Optimization Derivative Method Ensemble Method
$ {\mathtt{J}}_{\text{TP}} $ GN IExKF IEKF
$ {\mathtt{J}}_{\text{DM}} $ LM ILM-DM EKI
$ {\mathtt{J}}_{\text{TP}} $ LM ILM-TP TEKI
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