Ensemble-based Data Assimilation (EDA) has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. To facilitate the management of EDA workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a software framework linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. We verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed from continuous temperature measurements. Both cases yield accurate estimations of the parameters compared to their synthetic truth. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for large-scale inverse modeling using the sequential ES-MDA.

基于集合的数据同化（EDA）已有效地用于通过逆模型在地下流动和运输问题中估计模型参数。为了简化EDA工作流程的管理并降低在地下科学中采用基于EDA的参数估计的障碍，我们开发了一个软件框架，将数据同化研究试验台（DART）与大规模并行的地下FLOw和TRANsport代码PFLOTRAN链接在一起。DART-PFLOTRAN支持基于多数据同化的集成平滑器（ES-MDA）的迭代EDA工作流，以提高对非线性正向问题的估计精度。我们使用两个合成案例验证了DART-PFLOTRAN中ES-MDA的实现，这两个案例旨在通过连续温度测量来估算整个河床的静态渗透率和动态交换通量。与它们的综合真实情况相比，两种情况都可以对参数进行准确的估计。借助Python和Fortran的代码库，DART-PFLOTRAN为使用顺序ES-MDA的大规模逆建模铺平了道路。