将抽水测试数据转化为地下水模型参数:揭示含水层异质性及其对区域模型参数化影响的工作流程

Neil Manewell, John Doherty, Phil Hayes
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引用次数: 0

摘要

地下水建模人员经常遇到的难题是,如何将含水层测试解释与区域模型使用的参数结合起来。这项任务因规模扩大、数据同化以及需要为数值模型参数分配先验概率分布以支持模型预测不确定性分析等问题而变得复杂。为了解决这个问题,我们引入了一个新的框架,以弥合含水层测试与区域模型之间的巨大尺度差异。该框架还考虑到了原始数据集的丢失以及含水层测试中经常出现的地质介质的异质性。使用精细的数值网格再现含水层测试,可以在任意复杂程度上随机表示现场的水力特性。然后,利用数据空间反演技术,在区域模型单元中加入按比例放大的、受含水层测试限制的数值模型属性。一个应用实例表明,以这种方式同化历史抽水试验解释可以相对较快地完成。此外,同化过程有可能对决策相关模型预测的后验均值产生重大影响。然而,在我们讨论的例子中,后验预测的不确定性并没有显著降低。这些结果凸显了进一步研究的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Translating pumping test data into groundwater model parameters: a workflow to reveal aquifer heterogeneities and implications in regional model parameterization
Groundwater modelers frequently grapple with the challenge of integrating aquifer test interpretations into parameters used by regional models. This task is complicated by issues of upscaling, data assimilation, and the need to assign prior probability distributions to numerical model parameters in order to support model predictive uncertainty analysis. To address this, we introduce a new framework that bridges the significant scale differences between aquifer tests and regional models. This framework also accounts for loss of original datasets and the heterogeneous nature of geological media in which aquifer testing often takes place. Using a fine numerical grid, the aquifer test is reproduced in a way that allows stochastic representation of site hydraulic properties at an arbitrary level of complexity. Data space inversion is then used to endow regional model cells with upscaled, aquifer-test-constrained realizations of numerical model properties. An example application demonstrates that assimilation of historical pumping test interpretations in this manner can be done relatively quickly. Furthermore, the assimilation process has the potential to significantly influence the posterior means of decision-pertinent model predictions. However, for the examples that we discuss, posterior predictive uncertainties do not undergo significant reduction. These results highlight the need for further research.
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