High-resolution characterization of dense non-aqueous phase liquid (DNAPL) source zone architecture (SZA) is crucial for developing remediation strategies. Inverse modeling approaches are widely used to characterize DNAPL SZA. However, unknown parameters (permeability and DNAPL saturation) are usually assumed as Gaussian distribution in traditional geostatistical inverse methods (e.g., ensemble smoother with multiple data assimilation, ESMDA). This assumption may not be true in channels of fluvial deposits. The presence of clay lenses will bring significant challenges in reconstructing the DNAPL SZA using geostatistical inverse methods based on the Gaussian assumption. Moreover, the DNAPL characterization also suffers from a limited number of borehole data in the field. In this study, we use ESMDA's variant, ensemble smoother with multiple data assimilation-direct sampling (ESMDA-DS), to improve the estimation of non-Gaussian permeability field and evaluate the benefits of improved permeability estimation on the reconstruction of DNAPL source zone. The inversion technique is based on the iterative inversion framework to incorporate the clay content information into the DNAPL characterization. The performance of our proposed framework is examined and discussed through a numerical synthetic case with challenging pool-dominated SZA. To overcome data scarcity, we integrated hydrogeological and geophysical (electrical resistance tomography) datasets. Results show that the permeability is characterized well using ESMDA-DS method compared with ESMDA method. With the improved permeability estimation, the DNAPL source zone can be characterized in higher resolution. Compared with ESMDA method, the root mean square error (RMSE) of permeability is reduced by 38.5% using the ESMDA-DS method. Then the estimation RMSE of DNAPL saturation is reduced by 24.9% by improved permeability estimation. We also demonstrated the significant benefits of improved permeability estimation on DNAPL depletion behavior and plume evolution. Numerical simulations demonstrate with the improved permeability estimation, the DNAPL depletion prediction is highly improved, the prediction RMSE of the DNAPL dissolved plume is reduced by 34.5% after 50 years.

致密非水相液体 (DNAPL) 源区结构 (SZA) 的高分辨率表征对于制定修复策略至关重要。逆向建模方法被广泛用于表征 DNAPL SZA。然而，未知参数（渗透率和 DNAPL 饱和度）通常被假定为传统地统计逆方法中的高斯分布（例如，多数据同化的集成平滑，ESMDA）。这种假设在河流沉积的通道中可能不成立。粘土透镜体的存在将为使用基于高斯假设的地质统计逆方法重建 DNAPL SZA 带来重大挑战。此外，DNAPL 表征还受到现场钻孔数据数量有限的影响。在本研究中，我们使用 ESMDA 的变体，使用多数据同化-直接采样 (ESMDA-DS) 集成更平滑，以改进非高斯渗透率场的估计并评估改进的渗透率估计对重建 DNAPL 源区的好处。反演技术基于迭代反演框架，将粘土含量信息纳入 DNAPL 表征。我们提出的框架的性能通过具有挑战性的池主导的 SZA 的数值合成案例进行了检查和讨论。为了克服数据稀缺的问题，我们整合了水文地质和地球物理（电阻层析成像）数据集。结果表明，与ESMDA方法相比，ESMDA-DS方法能较好地表征渗透率。随着改进的渗透率估计，DNAPL 源区可以以更高的分辨率进行表征。与 ESMDA 方法相比，均方根误差 (使用 ESMDA-DS 方法，渗透率的RMSE降低了 38.5%。然后通过改进渗透率估计，DNAPL 饱和度的估计RMSE降低了 24.9%。我们还展示了改进渗透率估计对 DNAPL 消耗行为和羽流演化的显着益处。数值模拟表明，随着渗透率估计的改进，DNAPL 耗竭预测大大提高，DNAPL 溶解羽流的预测RMSE在 50 年后降低了 34.5%。