A novel approach for estimating hydraulic conductivity of non-Gaussian aquifer

SUN Meng; LUO Qiankun; KONG Zhiwei; GUO Ming; LIU Mingli; QIAN Jiazhong

doi:10.16030/j.cnki.issn.1000-3665.202308022

2024 Vol. 51, No. 3

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Article navigation > Hydrogeology & Engineering Geology > 2024 > 51(3): 23-33

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SUN Meng, LUO Qiankun, KONG Zhiwei, GUO Ming, LIU Mingli, QIAN Jiazhong. A novel approach for estimating hydraulic conductivity of non-Gaussian aquifer[J]. Hydrogeology & Engineering Geology, 2024, 51(3): 23-33. doi: 10.16030/j.cnki.issn.1000-3665.202308022

Citation:

SUN Meng, LUO Qiankun, KONG Zhiwei, GUO Ming, LIU Mingli, QIAN Jiazhong. A novel approach for estimating hydraulic conductivity of non-Gaussian aquifer[J]. Hydrogeology & Engineering Geology, 2024, 51(3): 23-33. doi: 10.16030/j.cnki.issn.1000-3665.202308022

A novel approach for estimating hydraulic conductivity of non-Gaussian aquifer

School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui　230009, China

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Corresponding author: LUO Qiankun, QKLuo@hfut.edu.cn

Received Date: 04 August 2023

Revised Date: 29 October 2023

Publish Date: 15 May 2024

Abstract

Abstract

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.
- data assimilation /
- non-Gaussian fields /
- parameter estimation /
- ensemble smoother with multiple data assimilation /
- normal-score transformation /
- hydraulic conductivity

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References

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