| Autor: |
Meng SUN, Qiankun LUO, Zhiwei KONG, Ming GUO, Mingli LIU, Jiazhong QIAN |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Shuiwen dizhi gongcheng dizhi, Vol 51, Iss 3, Pp 23-33 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1000-3665 |
| DOI: |
10.16030/j.cnki.issn.1000-3665.202308022 |
| Popis: |
The ensemble Kalman filter (EnKF) is one of the most widely used data assimilation methods. However, it exhibits limitations in handling non-Gaussian problems. To effectively address such issues and accurately describe the connectivity of aquifers, a novel approach named NS-ES-MDA is developed in this study. The proposed NS-ES-MDA synergistically combines the normal-score transformation (NST) with ensemble smoother with multiple data assimilation (ES-MDA). Through comparative experiments, the efficacy of NS-ES-MDA in estimating the hydraulic conductivity of non-Gaussian distributed aquifers is demonstrated. By assimilating the same dataset, NS-ES-MDA exhibits approximately 34% improvement in parameter estimation accuracy and about 35% enhancement in computational efficiency compared to the restart normal-score ensemble Kalman filter (rNS-EnKF). Furthermore, the NS-ES-MDA shows case robustness against the “equifinality” and displays remarkable updating capabilities, which leads to more precise parameter estimates. This study provides an effective solution for parameter estimation in non-Gaussian distributed aquifers. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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