New Insights Into Impact Glass Formation and Evolution Using Machine Learning and Aerodynamic Levitation Laser Heating Experiments
dc.contributor.advisor | Macris, Catherine | |
dc.contributor.author | Marrs, Ian James | |
dc.contributor.other | Barth, Andrew | |
dc.contributor.other | Druschel, Gregory | |
dc.date.accessioned | 2022-10-04T13:55:36Z | |
dc.date.available | 2022-10-04T13:55:36Z | |
dc.date.issued | 2022-09 | |
dc.degree.date | 2022 | en_US |
dc.degree.discipline | Department of Earth Science | en |
dc.degree.grantor | Indiana University | en_US |
dc.degree.level | M.S. | en_US |
dc.description | Indiana University-Purdue University Indianapolis (IUPUI) | en_US |
dc.description.abstract | Impact processes, where a meteor strikes a planetary body’s surface, are ubiquitous in the Solar System. These highly energetic events require study by both computational methods and experimental investigation. An impact process of particular interest to our study is the impact plume, a collection of vaporized rock and superheated gases that is produced during an impact event. Tektites are silica rich (roughly rhyolitic), extremely dry, and often contain both lechatelierite inclusions (amorphous SiO2) and flow textures (schlieren) and are an impact product of particular interest to this study. Tektites likely form either very early in the impact process or within the impact plume itself as condensates, and therefore offer a unique insight into the early stages of the impact cratering process. Here, we present both the results of the statistical analysis of published tektite geochemistry and the geochemical analysis of a variety of glasses produced in an aerodynamic levitation laser furnace. The major findings of the statistical analysis are that the variance of tektite geochemistry is broadly controlled by MgO, CaO, K2O, and Na2O, that the Australasian strewn field (an extensive region of tektite distribution) is best subdivided into five geochemical subgroups, and that random forest classification models can predict the strewn field or geochemical subgroup of an unknown tektite with >94% accuracy. In terms of our heating experiments, in nearly all cases, Na2O and K2O are rapidly lost from the melt due to evaporation, while Al2O3, CaO, and TiO2 become progressively enriched. Volatility is far more dependent on peak heating temperature than on heating time. Additionally, the chemical constituents of basalts are less readily volatilized than those of rhyolites or loess, with few exceptions. We also find that the volatility of the chemical constituents of non-standard samples is far more variable than for standard samples and that oxygen fugacity has a strong influence over elemental volatility in the aerodynamic levitation laser furnace. Changes in oxygen fugacity can either result in variable, exaggerated, or even opposite volatility trends depending on the material and oxide in question. | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/30177 | |
dc.identifier.uri | http://dx.doi.org/10.7912/C2/3033 | |
dc.language.iso | en_US | en_US |
dc.rights | Attribution-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | Tektites | en_US |
dc.subject | Machine learning | en_US |
dc.subject | High temperature geochemistry | en_US |
dc.subject | Geologic glass formation | en_US |
dc.title | New Insights Into Impact Glass Formation and Evolution Using Machine Learning and Aerodynamic Levitation Laser Heating Experiments | en_US |
dc.type | Thesis | en |
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