Revising the stochastic iterative ensemble smoother
Tóm tắt
Từ khóa
Tài liệu tham khảo
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, 2016. a, b
Bardsley, J. M., Solonen, A., Haario, H., and Laine, M.: Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems, SIAM J. Sci. Comput., 36, A1895–A1910, 2014. a
Bocquet, M.: Localization and the iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 142, 1075–1089, 2016. a
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304504, https://doi.org/10.1080/16000870.2017.1304504, 2017. a
Bocquet, M. and Sakov, P.: Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems, Nonlin. Processes Geophys., 19, 383–399, https://doi.org/10.5194/npg-19-383-2012, 2012. a, b, c
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 2013. a, b
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 140, 1521–1535, 2014. a, b, c, d
Bocquet, M., Raanes, P. N., and Hannart, A.: Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation, Nonlin. Processes Geophys., 22, 645–662, https://doi.org/10.5194/npg-22-645-2015, 2015. a
Bonavita, M., Isaksen, L., and Hólm, E.: On the use of EDA background error variances in the ECMWF 4D-Var, Q. J. Roy. Meteor. Soc., 138, 1540–1559, 2012. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, Wiley Interdisciplinary Reviews: Climate Change, 9, e535, 2018. a
Chen, Y. and Oliver, D. S.: Ensemble randomized maximum likelihood method as an iterative ensemble smoother, Math. Geosci., 44, 1–26, 2012. a, b, c, d
Chen, Y. and Oliver, D. S.: History Matching of the Norne Full Field Model Using an Iterative Ensemble Smoother-(SPE-164902), in: 75th EAGE Conference & Exhibition incorporating SPE EUROPEC, 2013a. a
Chen, Y. and Oliver, D. S.: Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification, Computat. Geosci., 17, 689–703, 2013b. a, b
Chen, Y. and Oliver, D. S.: Localization and regularization for iterative ensemble smoothers, Computat. Geosci., 21, 13–30, 2017. a
Emerick, A. A.: Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data, Computat. Geosci., 22, 1–12, 2018. a
Emerick, A. A. and Reynolds, A. C.: Ensemble smoother with multiple data assimilation, Computat. Geosci., 55, 3–15, 2013a. a
Emerick, A. A. and Reynolds, A. C.: Investigation of the sampling performance of ensemble-based methods with a simple reservoir model, Computat. Geosci., 17, 325–350, 2013b. a, b
Evensen, G.: Sampling strategies and square root analysis schemes for the EnKF, Ocean Dynam., 54, 539–560, 2004. a
Evensen, G.: Analysis of iterative ensemble smoothers for solving inverse problems, Computat. Geosci., 22, 885–908, 2018. a
Evensen, G.: Accounting for model errors in iterative ensemble smoothers, Computat. Geosci., 23, 761–775, https://doi.org/10.1007/s10596-019-9819-z, 2019. a
Fillion, A., Bocquet, M., and Gratton, S.: Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 25, 315–334, https://doi.org/10.5194/npg-25-315-2018, 2018. a
Gu, Y. and Oliver, D. S.: An iterative ensemble Kalman filter for multiphase fluid flow data assimilation, SPE J., 12, 438–446, 2007. a, b, c
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, 2007. a
Iglesias, M. A.: Iterative regularization for ensemble data assimilation in reservoir models, Computat. Geosci., 19, 177–212, 2015. a
Jardak, M. and Talagrand, O.: Ensemble variational assimilation as a probabilistic estimator – Part 1: The linear and weak non-linear case, Nonlin. Processes Geophys., 25, 565–587, https://doi.org/10.5194/npg-25-565-2018, 2018. a
Jazwinski, A. H.: Stochastic Processes and Filtering Theory, vol. 63, Academic Press, 1970. a
Kepert, J. D.: On ensemble representation of the observation-error covariance in the Ensemble Kalman Filter, Ocean Dynam., 54, 561–569, 2004. a
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P.: Optimization by simulated annealing, Science, 220, 671–680, 1983. a
Kitanidis, P. K.: Quasi-linear geostatistical theory for inversing, Water Resour. Res., 31, 2411–2419, 1995. a
Le, D. H., Emerick, A. A., and Reynolds, A. C.: An adaptive ensemble smoother with multiple data assimilation for assisted history matching, SPE J., 21, 2–195, 2016. a
Liu, Y., Haussaire, J.-M., Bocquet, M., Roustan, Y., Saunier, O., and Mathieu, A.: Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides, Q. J. Roy. Meteor. Soc., 143, 2886–2901, 2017. a
Livings, D. M., Dance, S. L., and Nichols, N. K.: Unbiased ensemble square root filters, Physica D, 237, 1021–1028, 2008. a
Lorenc, A. C.: Development of an Operational Variational Assimilation Scheme, Journal of the Meteorological Society of Japan, Series. II, 75 (Special issue: data assimilation in meteorology and oceanography: theory and practice), 339–346, 1997. a
Lorenz, E. N.: Predictability: A problem partly solved, in: Proc. ECMWF Seminar on Predictability, vol. 1, 1–18, Reading, UK, 1996. a
Luo, X., Stordal, A. S., Lorentzen, R. J., and Naevdal, G.: Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications, SPE J., 20, 962–982, 2015. a
Ma, X., Hetz, G., Wang, X., Bi, L., Stern, D., and Hoda, N.: A robust iterative ensemble smoother method for efficient history matching and uncertainty quantification, in: SPE Reservoir Simulation Conference, Society of Petroleum Engineers, 2017. a
Maciejewski, A. A. and Klein, C. A.: Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments, The international journal of robotics research, 4, 109–117, 1985. a
Mandel, J., Bergou, E., Gürol, S., Gratton, S., and Kasanický, I.: Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method, Nonlin. Processes Geophys., 23, 59–73, https://doi.org/10.5194/npg-23-59-2016, 2016. a
Morzfeld, M., Hodyss, D., and Poterjoy, J.: Variational particle smoothers and their localization, Q. J. Roy. Meteor. Soc., 144, 806–825, 2018. a
Muirhead, R. J.: Aspects of multivariate statistical theory, John Wiley & Sons, Inc., New York, wiley Series in Probability and Mathematical Statistics, 1982. a
Oliver, D. S.: Metropolized randomized maximum likelihood for improved sampling from multimodal distributions, SIAM/ASA Journal on Uncertainty Quantification, 5, 259–277, 2017. a
Oliver, D. S. and Chen, Y.: Recent progress on reservoir history matching: a review, Computat. Geosci., 15, 185–221, 2011. a
Oliver, D. S., Reynolds, A. C., and Liu, N.: Inverse Theory for Petroleum Reservoir Characterization and History Matching, Cambridge University Press, 2008. a
Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D. J., and Yorke, J. A.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, 2004. a, b
Pires, C., Vautard, R., and Talagrand, O.: On extending the limits of variational assimilation in nonlinear chaotic systems, Tellus A, 48, 96–121, 1996. a
Raanes, P. N., Bocquet, M., and Carrassi, A.: Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures, Q. J. Roy. Meteor. Soc., 145, 53–75, https://doi.org/10.1002/qj.3386, 2019. a
Rafiee, J. and Reynolds, A. C.: Theoretical and efficient practical procedures for the generation of inflation factors for ES-MDA, Inverse Problems, 33, 115003, https://doi.org/10.1088/1361-6420/aa8cb2, 2017. a
Reynolds, A. C., Zafari, M., and Li, G.: Iterative forms of the ensemble Kalman filter, in: 10th European Conference on the Mathematics of Oil Recovery, 2006. a, b, c
Sacher, W. and Bartello, P.: Sampling errors in ensemble Kalman filtering. Part I: Theory, Mon. Weather Rev., 136, 3035–3049, 2008. a
Sakov, P. and Bertino, L.: Relation between two common localisation methods for the EnKF, Computat. Geosci., 15, 225–237, 2011. a
Sakov, P. and Oke, P. R.: Implications of the form of the ensemble transformation in the ensemble square root filters, Mon. Weather Rev., 136, 1042–1053, 2008. a, b, c
Sakov, P., Oliver, D. S., and Bertino, L.: An Iterative EnKF for Strongly Nonlinear Systems, Mon. Weather Rev., 140, 1988–2004, 2012. a, b, c, d, e, f
Sakov, P., Haussaire, J.-M., and Bocquet, M.: An iterative ensemble Kalman filter in the presence of additive model error, Q. J. Roy. Meteor. Soc., 144, 1297–1309, 2018. a, b
Stordal, A. S.: Iterative Bayesian inversion with Gaussian mixtures: finite sample implementation and large sample asymptotics, Computat. Geosci., 19, 1–15, 2015. a
Tian, X., Xie, Z., and Dai, A.: An ensemble-based explicit four-dimensional variational assimilation method, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2008JD010358, 2008. a
Trefethen, L. N. and Bau III, D.: Numerical linear algebra, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1997. a
van Leeuwen, P. J.: Comment on “Data assimilation using an ensemble Kalman filter technique”, Mon. Weather Rev., 127, 1374–1377, 1999. a
Zafari, M. and Reynolds, A. C.: Assessing the uncertainty in reservoir description and performance predictions with the ensemble Kalman filter, Master's thesis, University of Tulsa, 2005. a