Conditioning Multi-Gaussian Groundwater Flow Parameters to Transient Hydraulic Head and Flowrate Data With Iterative Ensemble Smoothers: A Synthetic Case Study
Dan-Thuy Lam, Jaouhar Kerrou, Philippe Renard, Hakim Benabderrahmane & Pierre Perrochet
| Résumé |
Over the last decade, data assimilation methods based on the
ensemble Kalman filter (EnKF) have been particularly explored in
various geoscience fields to solve inverse problems. Although this
type of ensemble methods can handle high-dimensional systems, they
assume that the errors coming from whether the observations or the
numerical model are multivariate Gaussian. To handle existing
non-linearities between the observations and the variables to
estimate, iterative methods have been proposed. In this paper, we
investigate the feasibility of using the ensemble smoother and two
iterative variants for the calibration of a synthetic 2D
groundwater model inspired by a real nuclear storage problem in
France. Using the same set of sparse and transient flow data, we
compare the results of each method when employing them to condition
an ensemble of multi-Gaussian groundwater flow parameter fields. In
particular, we explore the benefit of transforming the state
observations to improve the parameter identification performed by
one of the two iterative algorithms tested. Despite the favorable
case of a multi-Gaussian parameter distribution addressed, we show
the importance of defining an ensemble size of at least 200 to
obtain sufficiently accurate parameter and uncertainty estimates
for the groundwater flow inverse problem considered. |
| Mots-clés |
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| Citation | Lam, D. T., Kerrou, J., Renard, P., Benabderrahmane, H., & Perrochet, P. (2020). Conditioning Multi-Gaussian Groundwater Flow Parameters to Transient Hydraulic Head and Flowrate Data With Iterative Ensemble Smoothers: A Synthetic Case Study. Frontiers in Earth Science, 8, 202-220. |
| Type | Article de périodique (Anglais) |
| Date de publication | 6-2020 |
| Nom du périodique | Frontiers in Earth Science |
| Volume | 8 |
| Pages | 202-220 |
| URL | https://doi.org/10.3389/feart.202000202 |