We study the question of how visual analysis can support the comparison of spatio-temporal ensemble data of liquid and gas flow in porous media. To this end, we focus on a case study, in which nine different research groups concurrently simulated the process of injecting CO2 into the subsurface. We explore different data aggregation and interactive visualization approaches to compare and analyze these nine simulations. In terms of data aggregation, one key component is the choice of similarity metrics that define the relation between the different simulations. We test different metrics and find that a fine-tuned machine-learning based metric provides the best visualization results. Based on that, we propose different visualization methods. For overviewing the data, we use dimensionality reduction methods that allow us to plot and compare the different simulations in a scatterplot. To show details about the spatio-temporal data of each individual simulation, we employ a space-time cube volume rendering. We use the resulting interactive, multi-view visual analysis tool to explore the nine simulations and also to compare them to data from experimental setups. Our main findings include new insights into ranking of simulation results with respect to experimental data, and the development of gravity fingers in simulations.

翻译：我们研究如何通过可视化分析支持多孔介质中气液两相流时空集成数据的比较。为此，我们聚焦于一项案例研究：九个不同研究团队同时模拟了向地下注入CO₂的过程。我们探索了不同的数据聚合与交互式可视化方法，以比较和分析这九个模拟结果。在数据聚合方面，关键组成部分之一是定义不同模拟间关系的相似性度量选择。我们测试了多种度量方法，发现基于微调机器学习的度量能提供最佳可视化效果。在此基础上，我们提出不同的可视化方法：为提供数据概览，采用降维方法将不同模拟结果绘制在散点图中进行比较；为展示每个独立模拟的时空数据细节，则运用时空立方体体渲染技术。最终构建的交互式多视图可视化分析工具，不仅用于探索九个模拟结果，还将其与实验装置数据进行对比。主要发现包括：揭示了模拟结果相对于实验数据的排序规律，以及模拟中重力指进现象的演化过程。