A contaminant source localisation strategy was developed considering unknown heterogeneous hydraulic conductivity field, unknown dispersivity and unknown location of a continuous contaminant source. The Gauss-Levenberg-Marquardt algorithm is combined with a data worth analysis to estimate the unknown parameters and identify the best locations of additional measurements. The data collection strategy is iterative, based on the ability of the additional dataset to decrease the uncertainties on the contaminant source location. Two 2D synthetic models are considered. The method is first illustrated with a simple model and a more complex model is then considered to evaluate the ability of the approach to locate the contaminant source from hydraulic heads and concentration data. This approach is parsimonious in terms of model runs and applicable to real cases. The results give a good estimate of the source location and the dispersivity, with acceptable NRMSE for each case. New observations introduced at each iteration decrease the standard deviation of the source location and improve the NRMSE. The estimated hydraulic conductivity field presents the same features as the original field.

考虑到未知的非均质水力传导率场，未知的分散性和未知的连续污染源位置，开发了污染源定位策略。Gauss-Levenberg-Marquardt算法与值得分析的数据相结合，以估计未知参数并确定其他测量的最佳位置。基于其他数据集减少污染物源位置不确定性的能力，数据收集策略是迭代的。考虑了两个2D合成模型。首先用一个简单的模型说明该方法，然后考虑使用一个更复杂的模型来评估该方法从液压头和浓度数据中定位污染物源的能力。这种方法在模型运行方面是简约的，适用于实际案例。结果对源位置和分散度进行了很好的估计，每种情况下的NRMSE都可以接受。在每次迭代中引入的新观测值减小了源位置的标准偏差并改善了NRMSE。估计的水力传导率场具有与原始场相同的特征。