  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.
