High-resolution characterization of complex dense non-aqueous phase liquid (DNAPL) contaminated sites is crucial for developing effective remediation strategies. The DNAPL source zone is usually characterized by hydraulic/partitioning tracer tomography (HPTT). However, the HPTT method may fail to capture the highly saturated pool-dominated DNAPL source zone architecture (SZA), because partitioning tracers tend to bypass the low-permeability zones where the pool DNAPL accumulates, resulting in a low-resolution DNAPL estimation. With a limited number of measurements, the estimation errors may be significant. To overcome these difficulties, time-lapse electrical resistivity tomography (ERT) was integrated with the partitioning interwell tracer test (PITT) and hydraulic tomography (HT) to characterize the pool-dominated DNAPL SZA. Herein, we proposed an iterative joint inversion framework coupling the multiphase flow model with the ERT forward model to estimate the heterogeneous permeability distribution and DNAPL SZA. Under this framework, permeability was estimated using the hydraulic head data from HT in stage 1, and the DNAPL SZA was subsequently estimated by assimilating both the PITT and ERT observations in stage 2. The permeability estimated from stage 1 was used as prior information for stage 2, and the DNAPL saturation estimated from stage 2 was served as prior information for stage 1 in the next loop to form an iterative loop to improve the estimation of both permeability and DNAPL SZA. The iterative joint inversion framework was evaluated in two numerical experiments with different heterogeneous structures by assimilating multi-source datasets, including hydraulic head, partitioning interwell tracer concentration, and electrical resistivity. Results show that with limited measurements of HPTT method, one can roughly capture the DNAPL distribution, missing the fine structure of the DNAPL SZA. In contrast, by incorporating multi-source datasets, the heterogeneous permeability and DNAPL SZA can be reconstructed with a higher resolution. Furthermore, the inversion accuracy of the DNAPL SZA improves progressively as the iteration proceeds, which demonstrates the advantage of utilizing complementary information from permeability and DNAPL distribution through the iteration framework. Comparing with the results without loop iteration, the estimation error is reduced by 17.3% for permeability and 8.6% for DNAPL saturation in Experiment 1; by 14.7% for permeability and 11.2% for DNAPL saturation in Experiment 2 through the iterative framework. To further evaluate our framework, we preformed the prediction of the depletion process of the DNAPL source zone and plume based on the estimated DNAPL SZA. Results show that using the iterative framework, the prediction of the SZA depletion is greatly improved, i.e., the estimation error of the dissolved downstream plume from the DNAPL source zone after 3 years is reduced by 20.9% in Experiment 1, and by 43.2% in Experiment 2, respectively, through the iterative framework. This significant improvement is because the iterative method can better capture the spread of DNAPL pool.

复杂致密非水相液体 (DNAPL) 污染场地的高分辨率表征对于制定有效的修复策略至关重要。DNAPL 源区通常以液压/分区示踪断层扫描 (HPTT) 为特征。然而，HPTT 方法可能无法捕获高度饱和的池主导的 DNAPL 源区结构 (SZA)，因为划分示踪剂往往会绕过池 DNAPL 积累的低渗透区，从而导致低分辨率的 DNAPL 估计。由于测量次数有限，估计误差可能很大。为了克服这些困难，延时电阻率断层扫描 (ERT) 与分区井间示踪剂测试 (PITT) 和水力断层扫描 (HT) 相结合，以表征池主导的 DNAPL SZA。在此处，我们提出了一个迭代联合反演框架，将多相流模型与 ERT 正演模型耦合，以估计非均质渗透率分布和 DNAPL SZA。在此框架下，渗透率是使用第 1 阶段 HT 的水头数据估算的，随后通过同化第 2 阶段的 PITT 和 ERT 观测结果估算 DNAPL SZA。第 1 阶段估算的渗透率用作阶段的先验信息2，从第 2 阶段估计的 DNAPL 饱和度作为下一个循环中第 1 阶段的先验信息，形成一个迭代循环，以改进渗透率和 DNAPL SZA 的估计。通过同化多源数据集，包括水头、划分井间示踪剂浓度和电阻率。结果表明，在 HPTT 方法的测量有限的情况下，可以粗略地捕获 DNAPL 分布，而忽略了 DNAPL SZA 的精细结构。相比之下，通过合并多源数据集，可以以更高的分辨率重建异质渗透率和 DNAPL SZA。此外，随着迭代的进行，DNAPL SZA 的反演精度逐渐提高，这表明通过迭代框架利用来自渗透率和 DNAPL 分布的互补信息的优势。与没有循环迭代的结果相比，实验1中渗透率的估计误差降低了17.3%，DNAPL饱和度的估计误差降低了8.6%；渗透率增加 14.7% 和 11。通过迭代框架，实验 2 中 DNAPL 饱和度为 2%。为了进一步评估我们的框架，我们根据估计的 DNAPL SZA 对 DNAPL 源区和羽流的消耗过程进行了预测。结果表明，使用迭代框架，SZA耗竭的预测大大提高，即，通过迭代框架，3年后DNAPL源区溶解的下游羽流的估计误差在实验1中分别降低了20.9%，在实验2中分别降低了43.2%。这种显着的改进是因为迭代方法可以更好地捕捉 DNAPL 池的传播。