基于集合卡尔曼滤波的潜水动态预测方法

Application of ensemble Kalman filter to phreatic water flow

将集合卡尔曼滤波(EnKF)方法拓展至补给条件下潜水流动的数据同化问题,通过同化水位、水力传导度和降雨补给等测量数据来更新模型状态、反演模型参数,探讨了在不同补给条件下测量数据对水力传导度和降雨入渗补给系数反演的影响,分析了不同类型测量数据在同化中的作用.结果表明:EnKF方法可以通过动态的测量数据改善对地下水模型参数的估计,方法在降雨补给量较大条件下可以取得更好的同化效果,说明在雨季等地下水运动变动剧烈时的测量数据价值更高,有长期水位动态测量数据时,可以通过水位观测值有效地反演出水力传导度和降雨入渗补给系数.

The data assimilation method based on ensemble Kalman filter（EnKF） is applied to solve the two-dimensional saturated groundwater flow problem. The influence of precipitation recharge is analyzed. The interaction between different types of observations and state variables is studied. The results show that the EnKF method can effectively improve the estimation of groundwater model parameters and the method performs better under higher rainfall supply. The saturated hydraulic conductivity Ks of the soil and subrainfall infiltration coefficient k can be effectively estimated with the availability of new water head observations H.