Detailed characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) is essential for designing efficient remediation strategies. However, it is difficult to characterize a highly irregular and localized SZA, because traditional drilling investigations provide limited information. With limited data, the estimation accuracy of traditional geostatistical methods is strongly affected by the parameterization of the prior description of the SZA. To improve characterization performance, we parameterized the DNAPL saturation field using a physics‐based approach. We trained a convolutional variational autoencoder (CVAE) using data from multiphase modeling that captures the physics of DNAPL infiltration. The trained CVAE network was used in SZA inversion to obtain an improved prior DNAPL saturation field, instead of the typical stationary prior covariances. We then integrated the CVAE network into an iterative ensemble smoother (ES), to formulate a joint inversion framework. To overcome difficulties from limited/sparse data, we incorporated hydrogeological and geophysical datasets in the proposed inversion framework. To evaluate the performance of our method, we conducted numerical experiments in a hypothetical heterogeneous aquifer with an intricate SZA. The results show that the CVAE was an effective and efficient parameterization method which can capture the DNAPL infiltration patterns better than a Gaussian prior. The improved prior, combined with multisource datasets, can result in better resolution, and overall improved SZA characterization. In contrast to the standard ES method, the proposed framework reconstructed the SZA more accurately. We also demonstrated that DNAPL depletion behavior and dissolved concentration profiles can be predicted accurately using the estimated SZA.

中文翻译：

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通过整合卷积变分自编码器和集成更平滑器对非平稳DNAPL源区进行水文地球物理表征

密实非水相液体（DNAPL）源区体系结构（SZA）的详细表征对于设计有效的补救策略至关重要。但是，由于传统的钻探研究提供的信息有限，因此很难描述高度不规则且局部化的SZA。在数据有限的情况下，SZA先前描述的参数化会严重影响传统地​​统计学方法的估计准确性。为了提高表征性能，我们使用基于物理学的方法对DNAPL饱和度字段进行了参数化。我们使用来自捕获DNAPL渗透物理过程的多相建模数据训练了卷积变分自编码器（CVAE）。经过训练的CVAE网络用于SZA反演，以获得改进的先前DNAPL饱和度场，而不是典型的平稳先验协方差。然后，我们将CVAE网络集成到迭代集成平滑器（ES）中，以制定联合反演框架。为了克服有限/稀疏数据带来的困难，我们将水文地质和地球物理数据集纳入了提出的反演框架。为了评估我们方法的性能，我们在带有复杂SZA的假设非均质含水层中进行了数值实验。结果表明，CVAE是一种有效且高效的参数化方法，与高斯先验方法相比，它可以更好地捕获DNAPL的渗透模式。改进的先验与多源数据集相结合，可以带来更好的分辨率，并全面改善SZA表征。与标准ES方法相比，该框架更准确地重建了SZA。