Identification and Quantitative Classification of Europa’s Microfeatures: Implications for Microfeature Formation Models and the Europa Clipper Flagship Mission
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Description
Jupiter’s moon Europa is an active target of research because of its unique geology and its potential for habitability. Europa’s icy chaos disrupts and transforms the previous terrain, suggesting melting is involved. Chaos occurs alongside several types of endogenic surface features. These microfeatures are under <100 km2 in area and include uplifts and domes, pits, spots, and hybrid features. The distribution of microfeatures is known in the ~10% of the Europa’s surface that are covered by the regional mosaics (“RegMaps”). The efforts to connect microfeature formation to any kind of heat transport in Europa are confounded because microfeatures are difficult to identify outside of RegMaps because of low image resolutions. Finding microfeatures outside of RegMaps would provide new observational constraints for microfeature formation models.
First, I mapped microfeatures across four of Europa’s RegMaps and validated them against other mapping datasets. Microchaos features are the most numerous, followed by pits, domes, then hybrids. Spots are the least common features, and the smallest. Next, I mapped features in low-resolution images that covered the E15RegMap01 area to determine error rates and sources of omission or misclassification for features mapped in low-resolution images. Of all features originally mapped in the RegMap, pits and domes were the least likely to be re-mapped or positively identified (24.2% and 5%, respectively). Chaos, spots, and hybrids were accurately classified over 70% of the time. Quantitatively classifying these features using discriminant function analysis yielded comparable values of accuracy when compared to a human mapper. Finally, nearest-neighbor clustering analyses were used to show that pits are clustered in all regions, while chaos, domes, and hybrids vary in terms of their spatial clustering.
This work suggests that the most likely processes for microfeature formations is either the evolution of liquid water sills within Europa’s ice shell or cryovolcanism. Future work extending to more areas outside of the RegMaps can further refine microfeature formation models. The detection of liquid water at or near the surface is a major goal of multiple upcoming Europa missions; this work provides predictions that can be directly tested by these missions to maximize their scientific return.
First, I mapped microfeatures across four of Europa’s RegMaps and validated them against other mapping datasets. Microchaos features are the most numerous, followed by pits, domes, then hybrids. Spots are the least common features, and the smallest. Next, I mapped features in low-resolution images that covered the E15RegMap01 area to determine error rates and sources of omission or misclassification for features mapped in low-resolution images. Of all features originally mapped in the RegMap, pits and domes were the least likely to be re-mapped or positively identified (24.2% and 5%, respectively). Chaos, spots, and hybrids were accurately classified over 70% of the time. Quantitatively classifying these features using discriminant function analysis yielded comparable values of accuracy when compared to a human mapper. Finally, nearest-neighbor clustering analyses were used to show that pits are clustered in all regions, while chaos, domes, and hybrids vary in terms of their spatial clustering.
This work suggests that the most likely processes for microfeature formations is either the evolution of liquid water sills within Europa’s ice shell or cryovolcanism. Future work extending to more areas outside of the RegMaps can further refine microfeature formation models. The detection of liquid water at or near the surface is a major goal of multiple upcoming Europa missions; this work provides predictions that can be directly tested by these missions to maximize their scientific return.