The joint identification of the parameters defining a contaminant source and the heterogeneous distribution of the hydraulic conductivities of the aquifer where the contamination took place is a difficult task. Previous studies have demonstrated the applicability of the restart normal-score ensemble Kalman filter (rNS-EnKF) in synthetic cases making use of sufficient hydraulic head and concentration data. This study shows an application of the same technique to a non-synthetic case under laboratory conditions and discusses the difficulties found on its application and the avenues taken to solve them. The method is first tested using a synthetic case that mimics the sandbox experiment to establish the minimum number of ensemble members and the best technique to prevent the filter collapsing. The synthetic case shows that among different techniques based on update damping and covariance inflation, the Bauser’s covariance inflation method works best in preventing filter collapse. Its application to the sandbox data shows that the rNS-EnKF can benefit from Bauser’s inflation to reduce the number of ensemble realizations substantially in comparison with a filter without inflation, arriving at a good joint identification of both the contaminant source and the spatial heterogeneity of the conductivities.

联合确定污染源的参数和发生污染的含水层水力传导率的不均匀分布是一项艰巨的任务。先前的研究表明，在充分利用液压头和浓度数据的情况下，重新启动的普通得分集成卡尔曼滤波器（rNS-EnKF）的适用性。这项研究显示了在实验室条件下将相同技术应用于非合成案例，并讨论了在其应用中发现的困难以及解决这些难题的途径。首先使用模拟沙盒实验的合成案例测试该方法，以确定最小数量的合奏成员和防止过滤器塌陷的最佳技术。综合案例表明，在基于更新阻尼和协方差膨胀的不同技术中，Bauser的协方差膨胀方法在防止滤波器崩溃方面效果最好。它在沙箱数据中的应用表明，与没有充气的过滤器相比，rNS-EnKF可以从鲍瑟的充气中受益，从而大大减少集成实现的数量，从而可以很好地共同识别污染物源和污染物的空间异质性。电导率。