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：我们研究视觉分析如何支持多孔介质中液体和气体流动的时空集合数据比较。为此，我们聚焦于一个案例研究，其中九个不同研究小组同时模拟了将二氧化碳注入地下的过程。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结果。在数据聚合方面，关键要素之一是选择定义不同模拟之间关系的相似性度量。我们测试了多种度量标准，发现基于微调机器学习方法的度量提供了最佳可视化效果。基于此，我们提出了不同的可视化方法。为概述数据，我们采用降维方法，使得不同模拟结果能够在散点图中进行绘制和比较。为展示每个独立模拟的时空数据细节，我们使用时空立方体体渲染技术。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结果，并将其与实验数据进行比较。主要发现包括：模拟结果相对于实验数据的排序新见解，以及模拟中重力指进现象的发展。