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| Hauptseite > Publikationsdatenbank > Investigating the pilot point ensemble Kalman filter for geostatistical inversion and data assimilation > print |
| Hauptseite > Publikationsdatenbank > Investigating the pilot point ensemble Kalman filter for geostatistical inversion and data assimilation > print |
| 001 | 902846 | ||
| 005 | 20230815122846.0 | ||
| 024 | 7 | _ | |a 10.1016/j.advwatres.2021.104010 |2 doi |
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| 100 | 1 | _ | |a Keller, Johannes |0 P:(DE-Juel1)184776 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Investigating the pilot point ensemble Kalman filter for geostatistical inversion and data assimilation |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2021 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Parameter estimation has a high importance in the geosciences. The ensemble Kalman filter (EnKF) allows parameter estimation for large, time-dependent systems. For large systems, the EnKF is applied using small ensembles, which may lead to spurious correlations and, ultimately, to filter divergence. We present a thorough evaluation of the pilot point ensemble Kalman filter (PP-EnKF), a variant of the ensemble Kalman filter for parameter estimation. In this evaluation, we explicitly state the update equations of the PP-EnKF, discuss the differences of this update equation compared to the update equations of similar EnKF methods, and perform an extensive performance comparison. The performance of the PP-EnKF is tested and compared to the performance of seven other EnKF methods in two model setups, a tracer setup and a well setup. In both setups, the PP-EnKF performs well, ranking better than the classical EnKF. For the tracer setup, the PP-EnKF ranks third out of eight methods. At the same time, the PP-EnKF yields estimates of the ensemble variance that are close to EnKF results from a very large-ensemble reference, suggesting that it is not affected by underestimation of the ensemble variance. In a comparison of the ensemble variances, the PP-EnKF ranks first and third out of eight methods. Additionally, for the well model and ensemble size 50, the PP-EnKF yields correlation structures significantly closer to a reference than the classical EnKF, an indication of the method’s skill to suppress spurious correlations for small ensemble sizes. |
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| 536 | _ | _ | |a DFG project 238370553 - Ensemble Kalman Filter zur Parameterschätzung in geklüfteten und fluviatilen geothermischen Reservoiren |0 G:(GEPRIS)238370553 |c 238370553 |x 1 |
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| 700 | 1 | _ | |a Hendricks-Franssen, Harrie-Jan |0 P:(DE-Juel1)138662 |b 1 |u fzj |
| 700 | 1 | _ | |a Nowak, Wolfgang |0 P:(DE-HGF)0 |b 2 |
| 773 | _ | _ | |a 10.1016/j.advwatres.2021.104010 |g Vol. 155, p. 104010 - |0 PERI:(DE-600)2023320-6 |p 104010 - |t Advances in water resources |v 155 |y 2021 |x 0309-1708 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/902846/files/2108.02164.pdf |y OpenAccess |
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