Coastal aquifers are a vital water source for the more than one billion people living in coastal regions around the globe. Due to the intensity of economic activities and density of population, these aquifers are highly susceptible not only to seawater intrusion, but also to anthropogenic contamination, which may contaminate the aquifer and submarine groundwater discharge. Identification and localization of contaminant source characteristics are needed to reduce contamination. The techniques of contaminant source identification are based on numerical models that require the knowledge of the hydrodynamic properties of aquifers. Thus, the challenging topic of contaminant source and aquifer characterization (CSAC) is widely developed in the literature. However, most of the existing studies are concerned with inland aquifers with relatively uniform groundwater flow. Coastal aquifers are influenced by density-driven seawater intrusion, tidal forces, and water injection and abstraction wells. These phenomena create complex flow and transport patterns, which render the CSAC especially challenging and may explain why CSAC has never been addressed in coastal settings. The presented study aims to provide an efficient methodology for the simultaneous identification of contaminant source characteristics and aquifer hydraulic conductivity in coastal aquifers. For this purpose, the study employs numerical modeling of density-dependent flow and multiple-species solute transport, to develop trained and validated artificial neural network metamodels, and then employs these metamodels in a version of the ensemble Kalman filter (EnKF) termed the ‘constrained restart dual EnKF (CRD-EnKF)’ algorithm. We show that this variant of the EnKF can be successfully applied to CSAC in the complex setting of coastal aquifers. Furthermore, the study analyzes the influence of common issues in CSAC monitoring, such as the effect of non-ideal monitoring network distributions, measurement errors, and multi-level vs. single level monitoring wells.

沿海含水层是生活在全球沿海地区的超过 10 亿人的重要水源。由于经济活动的强度和人口密度，这些含水层不仅对海水高度敏感入侵，但也会造成人为污染，这可能会污染含水层和海底地下水排放。需要对污染源特征进行识别和定位，以减少污染。污染物源识别技术基于需要了解含水层水动力特性的数值模型。因此，污染源和含水层表征（CSAC）这一具有挑战性的话题在文献中得到了广泛的发展。然而，现有的大多数研究都关注地下水流相对均匀的内陆含水层。. 沿海含水层受到密度驱动的海水入侵、潮汐力以及注水和抽水井的影响。这些现象产生了复杂的流动和运输模式，这使得 CSAC 尤其具有挑战性，并且可以解释为什么 CSAC 从未在沿海环境中得到解决。本研究旨在为同时识别沿海含水层中的污染物源特征和含水层导水率提供一种有效的方法。为此，该研究采用密度相关流动和多物种溶质传输，以开发经过训练和验证的人工神经网络元模型，然后在集成版本中使用这些元模型卡尔曼滤波器 (EnKF) 称为“约束重启双 EnKF (CRD-EnKF)”算法。我们表明，EnKF 的这种变体可以成功地应用于沿海含水层复杂环境中的 CSAC。此外，研究分析了CSAC监测中常见问题的影响，如监测网络分布不理想、测量误差、多级与单级监测井的影响。