水文地质学
地下水模型
地下水
含水层
地下水流
导水率
可识别性
反演(地质)
地质学
土壤科学
计算机科学
环境科学
岩土工程
地貌学
机器学习
构造盆地
土壤水分
作者
Sarah K. Marshall,Peter G. Cook,Craig T. Simmons,Leonard F. Konikow,Shawan Dogramaci
摘要
Abstract Hydrogeological barriers can significantly impact groundwater model predictions. They are, however, often excluded from, or misrepresented in, groundwater models if their presence is unknown or their properties are poorly constrained. Here we show that sharp barriers can be included in groundwater model inversion, even where their presence is uncertain. We describe an approach utilizing "phantom structures"—randomly located, linear groups of model cells assigned a unique hydraulic conductivity value—to improve identifiability of barriers. Algorithmic parameter estimation using model‐independent parameter estimation code (PEST) is implemented to determine model structures that best match the hydraulic head and groundwater age observation data from a hypothetical aquifer. Our results show that for a series of case studies, this method was successful in inferring the appropriate location and properties of hydrogeological barriers, when that barrier was not aligned with the dominant flow direction. We compare these results to model inversion using traditional pilot points. The phantom structures approach shows promise in identifying hydrogeological structures and in reproducing groundwater flow across a model domain. Our results demonstrate that the geometric properties of geological structures can remain flexible in a model inversion. This is a step toward reducing conceptual model uncertainty where the presence and properties of hydrogeologic barriers are undefined.
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