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：我们研究了视觉分析如何支持多孔介质中液体与气体流动的时空集成数据比较。为此，我们聚焦于一项案例研究，其中九个不同研究团队同时模拟了将CO2注入地下的过程。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。在数据聚合方面，一个关键组成部分是定义不同模拟之间关系的相似度度量选择。我们测试了多种度量，发现基于机器学习微调的度量能提供最佳可视化结果。基于此，我们提出了不同的可视化方法。为总览数据，我们采用降维方法，将不同模拟绘制于散点图中进行比较。为展示每个模拟的时空数据细节，我们使用了时空立方体体渲染。通过生成的交互式多视图视觉分析工具，我们探索了九个模拟，并将其与实验设置数据进行比较。主要发现包括：针对实验数据对模拟结果进行排序的新见解，以及模拟中重力指状现象的发展。