A geostatistical approach to contaminant source identification
A geostatistical approach to contaminant source identification
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DOI：
10.1029/96wr03753
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发表时间：
1997-04
影响因子：
5.4
通讯作者：
M. Snodgrass;P. Kitanidis
中科院分区：
文献类型：
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作者：
M. Snodgrass;P. Kitanidis
提出了一种污染源估算的地质统计学方法。问题是估计一个保守的溶质释放后的某个时间点的浓度测量的释放历史。一个贝叶斯框架之后，得出最佳估计和量化的估计误差。讨论了这种方法与常用的正则化和插值格式之间的关系。该方法的性能证明了在一个简单的一维均匀介质中的传输，虽然该方法是直接适用于在二维或三维域中的传输。该方法产生的释放历史和置信区间的最佳估计。释放历史的有条件实现生成，这在可视化和风险评估中是有用的。该方法的性能与稀疏数据和大的测量误差进行了检查。重点放在制定一个计算效率的方式估计方法。该方法不需要逆矩阵的大小取决于用于解决溶质释放历史的网格大小。模型验证的问题得到解决。
A geostatistical approach to contaminant source estimation is presented. The problem is to estimate the release history of a conservative solute given point concentration measurements at some time after the release. A Bayesian framework is followed to derive the best estimate and to quantify the estimation error. The relation between this approach and common regularization and interpolation schemes is discussed. The performance of the method is demonstrated for transport in a simple one‐dimensional homogeneous medium, although the approach is directly applicable to transport in two‐ or three‐dimensional domains. The methodology produces a best estimate of the release history and a confidence interval. Conditional realizations of the release history are generated that are useful in visualization and risk assessment. The performance of the method with sparse data and large measurement error is examined. Emphasis is placed on formulating the estimation method in a computationally efficient manner. The method does not require the inversion of matrices whose size depends on the grid size used to resolve the solute release history. The issue of model validation is addressed.