Our study is aimed at assessing the extent at which relying on differing geostatistical approaches may affect characterization of the connectivity of geomaterials (or facies) and, in turn, model calibration outputs in highly heterogeneous aquifers. We set our study within a probabilistic framework, by relying on a numerical Monte Carlo (MC) approach. The reconstruction of the spatial distribution of geomaterials and flow simulations are patterned after a field scenario corresponding to the aquifer system serving the city of Bologna (Northern Italy). Two collections of MC realizations of facies distributions, conditional on available lithological data, are generated through two alternative geostatistically-based techniques, i.e., Sequential Indicator and Transition-Probability simulation. Hydraulic conductivity values of the least- and most-conductive facies are estimated within each MC simulation in the context of a Maximum Likelihood (ML) approach by considering available piezometric data. We provide evidence that the choice of the facies reconstruction technique (1) impacts the degree of connectivity of facies whose proportions are close to the percolation threshold while (2) is not sensibly affecting the connectivity associated with facies whose proportions are much larger than the percolation threshold. By relying on the unique (lithological and hydrological) data-set at our disposal, we also explore the performance of ML-based model identification criteria to (1) discriminate amongst competitive facies reconstruction geostatistical models and (2) quantify the (posterior probabilistic) weight associated with each model. We then show that ML-based model averaging provides estimates of hydraulic heads which are slightly more in agreement with available data when compared to the best-performing realization in the T-PROGS set than considering its counterpart associated with the SISIM-based collection.

我们的研究旨在评估依赖不同地统计方法的程度可能会影响岩土（或相）连通性的表征。），然后在高度异构的含水层中对校准输出进行建模。我们依靠数值蒙特卡罗（MC）方法在概率框架内进行研究。在与服务于博洛尼亚市（意大利北部）的含水层系统相对应的野外情景下，对岩土材料的空间分布重建和流量模拟进行了建模。通过两种可选的基于地统计学的技术，即相继指标和过渡概率模拟，生成了以可用岩性数据为条件的相分布的MC实现的两个集合。在最大似然（ML）方法的背景下，通过考虑可用的测压数据，可以在每次MC模拟中估算导电性最低和导电性最高的相的水力传导率值。我们提供的证据表明，相重构技术的选择（1）影响比例接近渗滤阈值的相的连通程度，而（2）不会显着影响与比例远大于渗滤的相相关的连通性阈。通过依靠我们独有的（岩性和水文）数据集，我们还探索了基于ML的模型识别标准的性能，以（1）区分竞争相重建地统计模型，以及（2）量化（后验概率）每个模型的重量。