Key Laboratory of Surficial Geochemistry of Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China.
Department of Civil and Environmental Engineering, Western University, London, Ontario N6A 5B9, Canada.
J Contam Hydrol. 2023 Sep;258:104240. doi: 10.1016/j.jconhyd.2023.104240. Epub 2023 Sep 1.
Toxic organic contaminants in groundwater are pervasive at many industrial sites worldwide. These contaminants, such as chlorinated solvents, often appear as dense non-aqueous phase liquids (DNAPLs). To design efficient remediation strategies, detailed characterization of DNAPL Source Zone Architecture (SZA) is required. Since invasive borehole-based investigations suffer from limited spatial coverage, a non-intrusive geophysical method, direct current (DC) resistivity, has been applied to image the DNAPL distribution; however, in clay-sand environments, the ability of DC resistivity for DNAPLs imaging is limited since it cannot separate between DNAPLs and surrounding clay-sand soils. Moreover, the simplified parameterization of conventional inversion approaches cannot preserve physically realistic patterns of SZAs, and tends to smooth out any sharp spatial variations. In this paper, the induced polarization (IP) technique is combined with DC resistivity (DCIP) to provide plausible DNAPL characterization in clay-sand environments. Using petrophysical models, the DCIP data is utilized to provide tomograms of the DNAPL saturation (S) and hydraulic conductivity (K). The DCIP-estimated K/S tomograms are then integrated with borehole measurements in a deep learning-based joint inversion framework to accurately parameterize the highly irregular SZA and provide a refined DNAPL image. To evaluate the performance of the proposed approach, we conducted numerical experiments in a heterogeneous clay-sand aquifer with a complex SZA. Results demonstrate the standalone DC resistivity method fails to infer the DNAPL in complex clay-sand environments. In contrast, the combined DCIP technique provides the necessary information to reconstruct the large-scale features of K/S fields, while integrating DCIP data with sparse but accurate borehole data results in a high resolution characterization of the SZA.
地下水中的有毒有机污染物在世界许多工业场所普遍存在。这些污染物，如氯代溶剂，通常表现为密集的非水相液体（DNAPL）。为了设计有效的修复策略，需要对 DNAPL 源区结构（SZA）进行详细的特征描述。由于侵入式钻孔调查的空间覆盖范围有限，因此已经应用了一种非侵入式地球物理方法，即直流（DC）电阻率，来对 DNAPL 分布进行成像；然而，在粘土-砂环境中，由于 DC 电阻率无法区分 DNAPL 和周围的粘土-砂土壤，因此其对 DNAPLs 成像的能力有限。此外，传统反演方法的简化参数化不能保留 SZA 的物理现实模式，并且往往会使任何尖锐的空间变化变得平滑。在本文中，感应极化（IP）技术与直流电阻率（DCIP）相结合，为粘土-砂环境中的 DNAPL 提供合理的特征描述。使用岩石物理模型，对 DCIP 数据进行处理，以提供 DNAPL 饱和度（S）和水力传导率（K）的断层扫描图像。然后，将 DCIP 估计的 K/S 断层扫描图像与深孔测量结果集成到基于深度学习的联合反演框架中，以准确参数化高度不规则的 SZA，并提供改进的 DNAPL 图像。为了评估所提出方法的性能，我们在具有复杂 SZA 的非均质粘土-砂含水层中进行了数值实验。结果表明，独立的直流电阻率方法无法推断复杂粘土-砂环境中的 DNAPL。相比之下，组合的 DCIP 技术提供了重建 K/S 场大尺度特征的必要信息，而将 DCIP 数据与稀疏但准确的钻孔数据集成在一起，可对 SZA 进行高分辨率的特征描述。
