Geodynamic Modeling Applied to Venus
Euen, Grant Thomas
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Modern geodynamic modeling is more complex than ever, and has been used to answer questions about Earth pertaining to the dynamics of the convecting mantle and core, layers humans have never directly interacted with. While the insights gleaned from these models cannot be argued, it is important to ensure calculations are understood and behaving correctly according to known math and physics. Here I perform several thermal 3-D spherical shell tests using the geodynamic code ASPECT, and compare the results against the legacy code CitcomS. I find that these two codes match to within 1.0% using a number of parameters. The application of geodynamic modeling is also traditionally to expand our understanding of Earth; however, even with a scarcity of data modern methods can provide insight into other planetary bodies. I use machine learning to show that coronae, circular features on the surface of the planet Venus, are not randomly distributed. I suggest the idea of coronae being fed by secondary mantle plumes in connected clusters. The entirety of the Venusian surface is poorly understood as well, with a large percentage being topographically smooth and much younger than the planet's hypothesized age. I use modeling to test the hypothesis of a large impact being responsible for a major resurfacing event in Venus's history, and find three distinct scenarios following impact: relatively little change, some localized change evolving into resurfacing through geologic time, or large-scale overturn and injection of heat deep into the Venusian mantle.
General Audience Abstract
Modern geodynamic modeling has been used to answer questions about Earth in wide-ranging fields. Despite technological improvements, it is important to ensure the calculations are understood and behaving correctly. Here I perform several tests using a code called ASPECT and compare the results against another code, CitcomS. I find that the two codes are in good agreement. Application of these techniques is also traditionally done for Earth, but modern methods can provide insight into other planets or moons as well. Coronae are circular features on the surface of Venus that are poorly understood. I use machine learning to show that these are not randomly distributed, and suggest a mechanism for the formation of clusters of coronae. The surface of Venus is also strange: it is both too flat and too young based on current ideas in planetary science. I use modeling to test whether a large impact could cause the details of Venus's surface we see today.
- Doctoral Dissertations