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dc.contributor.advisorPalin, Richard M.
dc.contributor.advisorBohrson, Wendy A.
dc.contributor.authorCone, Kim A.
dc.date.accessioned2023-12-12T22:52:56Z
dc.date.available2023-12-12T22:52:56Z
dc.date.issued2023
dc.identifierCone_mines_0052E_12673.pdf
dc.identifierT 9600
dc.identifier.urihttps://hdl.handle.net/11124/178670
dc.descriptionIncludes bibliographical references.
dc.description2023 Summer.
dc.description.abstractMagma oceans are a common stage in the evolution of terrestrial bodies in the inner solar system and likely elsewhere. Their early behavior—how they convect, cool, and crystallize—helps determine the initial structure of planets and how evolution might proceed. The Earth’s Moon is believed to have experienced an early global magma ocean stage (a direct result of the Moon-forming impact), and attempts to reconcile the Moon’s current structure with how an ancient lunar magma ocean may have initially solidified have provided only general constraints. Previous investigations into lunar magma ocean solidification and consequent compositional stratification have hinged primarily on high pressure-temperature experimental techniques and joint inversion methods that included various assumed bulk silicate Moon compositions. Although the approaches provide valuable insight into a global lunar structure the resolution of experimentally-based approaches is typically. Numerical modeling using phase equilibria calculators provides an opportunity to investigate magma ocean cooling and crystallization more efficiently than experimental approaches and is one of the primary tools used here, forming one of the three projects in this thesis. The other two projects are intertwined—the creation of a lunar basalt database and unsupervised machine learning employing the database—and semi-quantitatively address the distribution of various basalt characteristics on the lunar nearside in an attempt to gain some insight into the subsurface. In general, this thesis uses numerical modelling to investigate aspects of lunar evolution spanning ~4.5 billion years, starting with an ancient molten Moon of Earth-like composition that cools and solidifies along a high melt-fraction path to the Moon’s current [mostly solid] state which records a history of punctuated nearside volcanism and surface-altering impacts. The machine learning work here is novel and the first of its kind in using lunar basalt characteristics to constrain lunar evolution, and only one other phase equilibria calculator model on lunar magma ocean solidification has been published at the time of this writing. The work in this thesis supports the idea of a cumulate mantle overturn, that some degree of mantle displacement, locally or globally, occurred during or at the very end of lunar magma ocean solidification.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2023 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectmachine learning
dc.subjectmagma ocean
dc.subjectmoon
dc.subjectphase equilibria
dc.titleInsights into lunar magma ocean solidification using machine learning and phase equilibria models
dc.typeText
dc.date.updated2023-11-30T05:07:38Z
dc.contributor.committeememberWendlandt, Richard F.
dc.contributor.committeememberBozdag, Ebru
dc.contributor.committeememberCannon, Kevin M.
dcterms.embargo.expires2024-05-29
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineGeology and Geological Engineering
thesis.degree.grantorColorado School of Mines
dc.rights.accessEmbargo Expires: 05/29/2024


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