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：我们研究了可视化分析如何支持比较多孔介质中液体和气体流动的时空集合数据。为此，我们关注一个案例研究，在该研究中，九个不同的研究小组同时对地下注入CO2的过程进行了模拟。我们探索了不同的数据汇总和交互式可视化方法来比较和分析这九种模拟。在数据汇总方面，一个关键组件是选择相似度度量来定义不同模拟之间的关系。我们测试了不同的度量标准，并发现一个精调的基于机器学习的指标提供了最好的可视化结果。基于此，我们提出了不同的可视化方法。为了概述数据，我们使用降维方法，可以将不同的模拟以散点图形式绘制和比较。为了展示每个单独模拟的时空数据细节，我们采用了空时立方体体渲染。我们使用生成的交互式、多视角可视化分析工具来探索这九种模拟，并将它们与实验数据进行比较。我们的主要发现包括对模拟结果与实验数据排名的新见解，以及模拟中重力指数的发展。