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- All Subjects: Europa
- Creators: Williams, David A.
Description
A thorough understanding of Europa's geology through the synergy of science and technology, by combining geologic mapping with autonomous onboard processing methods, enhances the science potential of future outer solar system missions. Mapping outlines the current state of knowledge of Europa's surface and near sub-surface, indicates the prevalence of distinctive geologic features, and enables a uniform perspective of formation mechanisms responsible for generating those features. I have produced a global geologic map of Europa at 1:15 million scale and appraised formation scenarios with respect to conditions necessary to produce observed morphologies and variability of those conditions over Europa's visible geologic history. Mapping identifies areas of interest relevant for autonomous study; it serves as an index for change detection and classification and aids pre-encounter targeting. Therefore, determining the detectability of geophysical activity is essential. Activity is evident by the presence of volcanic plumes or outgassing, disrupted surface morphologies, or changes in morphology, color, temperature, or composition; these characteristics reflect important constraints on the interior dynamics and evolutions of planetary bodies. By adapting machine learning and data mining techniques to signatures of plumes, morphology, and spectra, I have successfully demonstrated autonomous rule-based response and detection, identification, and classification of known events and features on outer planetary bodies using the following methods: 1. Edge-detection, which identifies the planetary horizon and highlights features extending beyond the limb; 2. Spectral matching using a superpixel endmember detection algorithm that identifies mean spectral signatures; and 3. Scale invariant feature transforms combined with supervised classification, which examines brightness gradients throughout an image, highlights extreme gradient regions, and classifies those regions based on a manually selected library of features. I have demonstrated autonomous: detection of volcanic plumes or jets at Io, Enceladus, and several comets, correlation between spectral signatures and morphological appearances of Europa's individual tectonic features, detection of ≤94% of known transient events on multiple planetary bodies, and classification of similar geologic features. Applying these results to conditions expected for Europa enables a prediction of the potential for detection and recommendations for mission concepts to increase the science return and efficiency of future missions to observe Europa.
ContributorsBunte, Melissa K (Author) / Bell, Iii, James F. (Thesis advisor) / Williams, David A. (Committee member) / Saripalli, Srikanth (Committee member) / Clarke, Amanda B. (Committee member) / Reynolds, Stephen J. (Committee member) / Christensen, Phillip R. (Committee member) / Arizona State University (Publisher)
Created2013
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.
ContributorsNoviello, Jessica (Author) / Rhoden, Alyssa R (Thesis advisor) / Christensen, Philip R. (Philip Russel) (Thesis advisor) / Williams, David A. (Committee member) / Robinson, Mark (Committee member) / Scowen, Paul (Committee member) / Arizona State University (Publisher)
Created2019