Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes within an aquifer. However, the values of input parameters that drive groundwater flow models are often highly uncertain due to subsurface heterogeneity and geologic complexity in combination with lack of measurements/unreliable measurements. This uncertainty affects the accuracy and reliability of model outputs. Therefore, parameters' uncertainty must be quantified before adopting the model as an engineering tool. In this study, we model the uncertain parameters as random variables and use a Bayesian inversion approach to obtain a posterior,data-informed, probability density function (pdf) for them: in particular, the likelihood function we consider takes into account both well measurements and our prior knowledge about the extent of the springs in the domain under study. To keep the modelistic and computational complexities under control, we assume Gaussianity of the posterior pdf of the parameters. To corroborate this assumption, we run an identifiability analysis of the model: we apply the inversion procedure to several sets of synthetic data polluted by increasing levels of noise, and we determine at which levels of noise we can effectively recover the "true value" of the parameters. We then move to real well data (coming from the Ticino River basin, in northern Italy, and spanning a month in summer 2014), and use the posterior pdf of the parameters as a starting point to perform an Uncertainty Quantification analysis on groundwater travel-time distributions.
翻译:地下水流模型通常用于计算地下水头、估计地下水流路径和旅行时间,并对含水层内的溶液运输过程提供洞察力。然而,驱动地下水流模型的输入参数值往往由于地表下层异质和地质复杂性,加上缺乏测量/不可靠的测量结果,因此,不确定性影响模型产出的准确性和可靠性。因此,参数的不确定性必须量化,然后才采用模型作为工程工具。在本研究中,我们将不确定参数作为随机变量进行模型模型模型模型的模型模型,并使用巴耶斯反向法方法,为它们获取一个后表层、数据知情、概率密度功能(pdf),特别是,我们所考虑的概率功能往往非常不确定,因为表层的测量和以前对所研究领域泉源范围的了解不够。为了控制模型和计算复杂性,我们假定参数的后部的参数具有可计量性。为了证实这一假设,我们对模型的可识别性参数进行了一种可辨度分析:我们采用转换程序,对若干组合成数据进行污染,方法是通过不断提高的海床的噪音和海拔参数,我们从时算取取取回的海床底的频率,我们可追溯到意大利的数据水平,我们可追溯到恢复了海床底的频率,我们从一个水平,然后恢复了海床底的海路的海流的海路的频率,我们可恢复了海路流的比值。我们从一个水平,我们可以确定一个水平,然后恢复了海流流流流流流流流的比值,在意大利的数据值,然后恢复到一个水平,然后又恢复到一个水平,我们算取取取取取取取取取取取取取取取取取。</s>