一种新的估计非高斯分布含水层渗透系数场的方法OA北大核心CSTPCD

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.