Berg Steven J, Illman Walter A
Department of Earth & Environmental Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.; Aquanty Inc., Waterloo, Ontario N2L 5C6, Canada.
Ground Water. 2015 Jan-Feb;53(1):71-89. doi: 10.1111/gwat.12159. Epub 2014 Jan 15.
Over the past several decades, different groundwater modeling approaches of various complexities and data use have been developed. A recently developed approach for mapping hydraulic conductivity (K) and specific storage (Ss ) heterogeneity is hydraulic tomography, the performance of which has not been compared to other more "traditional" methods that have been utilized over the past several decades. In this study, we compare seven methods of modeling heterogeneity which are (1) kriging, (2) effective parameter models, (3) transition probability/Markov Chain geostatistics models, (4) geological models, (5) stochastic inverse models conditioned to local K data, (6) hydraulic tomography, and (7) hydraulic tomography conditioned to local K data using data collected in five boreholes at a field site on the University of Waterloo (UW) campus, in Waterloo, Ontario, Canada. The performance of each heterogeneity model is first assessed during model calibration. In particular, the correspondence between simulated and observed drawdowns is assessed using the mean absolute error norm, (L1 ), mean square error norm (L2 ), and correlation coefficient (R) as well as through scatterplots. We also assess the various models on their ability to predict drawdown data not used in the calibration effort from nine pumping tests. Results reveal that hydraulic tomography is best able to reproduce these tests in terms of the smallest discrepancy and highest correlation between simulated and observed drawdowns. However, conditioning of hydraulic tomography results with permeameter K data caused a slight deterioration in accuracy of drawdown predictions which suggests that data integration may need to be conducted carefully.
在过去几十年中，已经开发出了各种复杂度和数据使用方式不同的地下水建模方法。一种最近开发的用于绘制水力传导率（K）和比储水系数（Ss）非均质性的方法是水力层析成像，其性能尚未与过去几十年使用的其他更“传统”的方法进行比较。在本研究中，我们比较了七种模拟非均质性的方法，它们分别是：（1）克里金法，（2）有效参数模型，（3）转移概率/马尔可夫链地质统计学模型，（4）地质模型，（5）基于局部K数据的随机反演模型，（6）水力层析成像，以及（7）基于局部K数据的水力层析成像，使用在加拿大安大略省滑铁卢市滑铁卢大学校园一个场地的五个钻孔中收集的数据。每个非均质性模型的性能首先在模型校准期间进行评估。具体而言，使用平均绝对误差范数（L1）、均方误差范数（L2）和相关系数（R）以及通过散点图来评估模拟水位下降和观测水位下降之间的对应关系。我们还评估了各种模型预测九次抽水试验中未用于校准工作的水位下降数据的能力。结果表明，就模拟水位下降和观测水位下降之间的最小差异和最高相关性而言，水力层析成像最能重现这些试验。然而，用水力渗透仪测得的K数据对水力层析成像结果进行校正导致水位下降预测精度略有下降，这表明数据整合可能需要谨慎进行。
