Field studies at terrestrial analogue sites represent an important contribution to the science of ocean worlds. The value of the science and technology investigations conducted at field analogue sites depends on the relevance of the analogue environment to the target ocean world. We accept that there are no perfect analogues for many of the unique environments represented by ocean worlds but suggest that a one-to-one matching of environmental characteristics and conditions is not crucial to the success or impact of the work. Instead, we must instead determine which processes and parameters are required to map directly to the target ocean world environment with high fidelity to address the science question or engineering challenge. Where there are discrepancies between the model and target environment, we must fully understand how those limitations impact the applicability of the study, and mitigate these where possible using alternative approaches. Here we present a two-step approach to 1) identify the most crucial processes and parameters associated with a given science question and 2) assess the fidelity of these processes and parameters at a proposed field site to those expected for the target ocean world. We demonstrate this approach in a test case evaluating three types of ocean world analogue environments with respect to a science question. Our proposed framework will not only enhance the scientific rigor of field research but also provide access to a broader range of field sites relevant to ocean worlds processes, enabling a greater diversity of ocean and geological science researchers.

Areas of Jupiter’s moon, Europa, contain irregular ice floes that are illustrative of the massive disruption, reorientation, and refreezing experienced on Europa’s surface as Jupiter’s gravity imparts immense tidal forces that heat the moon. In recent years, various machine learning programs have been used to detect surface features on planetary bodies. Most commonly, such software works to count craters for estimating planetary surface age or to map sand-filled dune fields whose shapes may indicate wind or weather patterns. Creating software to automatically detect Europa’s jigsaw-like ice floes will accelerate scientific analysis of such terrains once higher resolution images of the moon arrive in the fall of 2022 from the Juno spacecraft and later from the forthcoming Europa Clipper mission. In this project, a U-net, a deep learning semantic segmentation model, was applied to images of the surface of Jupiter’s moon Europa taken by the Galileo spacecraft to detect ice floes in the moon’s Chaos Terrains. To measure the quality of the program developed, the Intersection over Union (IoU), a metric that measures the goodness of fit for semantic segmentation, was calculated. Throughout the course of the project, the IoU increased from a value of 0.0012 to 0.286 by adjusting hyperparameters including learning rate and epochs. Adjusting how the data was labeled also improved performance, functioning best when ice floes were hand-labeled using loose-fitting polygons rather than exact edge-mapping. As Galileo faced transmission issues, the usable dataset was limited to 23 images, 19 of which were used for training and 4 for testing. In the coming months, higher quality augmented data should provide additional training images that should further improve the performance of the U-net. Once the algorithm is sufficiently capable of identifying floes in the Chaos Terrains, it may later assist in selection of regions of interest for further study on Europa or even landing sites for a future proposed lander.