For all applications, subsurface models should be consistent with all available geological and geophysical knowledge. Current practices for synergistic interpretation of geological and geophysical approaches often rely on purely qualitative comparisons, resulting sometimes in inconsistent findings. This study introduces a procedure for a statistical and geo-constrained clustering of electrical resistivity data derived from Electrical Resistivity Tomography (ERT) to address this gap, providing a quantitative parameterization for site-specific geoelectrical signatures of litho-stratigraphic architectures. Seventeen boreholes and three ERT surface profiles were employed to link geophysical inversion results to geological criteria. Core samples allowed grain size analyses, while geological-statistical clustering of electrical resistivity, driven by the observation of stratigraphic contacts in drilled boreholes, established a parametric relationship between geology and geophysics. The iterative clustering procedure, utilizing a classification algorithm, geological boundary constraints, and granulometric analyses, discriminated six distinct lithological clusters, capturing the lateral and vertical heterogeneity of shallow deposits. Subsequent spatial grouping of anthropogenic materials delineated lithological structures and facilitated the classification and identification of filling materials, silty sands, clayey sands, and clays and silts, each exhibiting distinct resistivity variations. The geo-driven geophysical clustering revealed complex lithological structures, especially paleo-channels, capturing their unique geoelectric footprints. The iterative clustering of geo-constrained resistivity data emerges as a powerful tool for subsurface exploration, contributing significantly to understanding lithological heterogeneity, quantifying statistically-based geoelectrical parametrization of shallow sediments, and evaluating the resistivity signature of different deposits. By bridging the gap between geology and geophysics, this data-driven approach establishes a benchmark for future applications. For instance, in the context of contaminated sites, it can be applied to identify pollutants versus geological heterogeneities.

中文翻译：

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电阻率数据的地理约束聚类揭示了浅层沉积物的异质岩性结构和独特的地电特征


对于所有应用，地下模型应与所有可用的地质和地球物理知识一致。当前地质和地球物理方法协同解释的实践通常依赖于纯粹的定性比较，有时会导致不一致的结果。本研究介绍了一种对电阻率断层扫描 (ERT) 得出的电阻率数据进行统计和地理约束聚类的程序，以解决这一差距，为岩石地层结构的特定地点地电特征提供定量参数化。采用十七个钻孔和三个 ERT 表面剖面将地球物理反演结果与地质标准联系起来。岩心样本可以进行粒度分析，而电阻率的地质统计聚类（由钻探钻孔中地层接触的观察驱动）建立了地质学和地球物理学之间的参数关系。迭代聚类过程利用分类算法、地质边界约束和粒度分析，区分了六个不同的岩性簇，捕获了浅层矿床的横向和纵向异质性。随后对人类活动物质进行空间分组，描绘了岩性结构，并促进了填充物质、粉砂、粘土砂、粘土和粉砂的分类和识别，每种物质都表现出明显的电阻率变化。地质驱动的地球物理聚类揭示了复杂的岩性结构，特别是古河道，捕获了其独特的地电足迹。 地理约束电阻率数据的迭代聚类成为地下勘探的强大工具，对了解岩性非均质性、量化浅层沉积物基于统计的地电参数化以及评估不同沉积物的电阻率特征做出了重大贡献。通过弥合地质学和地球物理学之间的差距，这种数据驱动的方法为未来的应用建立了基准。例如，在污染场地的背景下，它可以用于识别污染物与地质异质性。