From Isotopes and Whole Rock Geochemistry to Machine Learning: Diving into the Plumbing System of Large Mafic Eruptions using a Diverse Geochemical Toolset to Investigate Magmatic Processes
| dc.contributor.advisor | Karlstrom, Leif | |
| dc.contributor.author | Hampton, Rachel | |
| dc.date.accessioned | 2023-03-24T18:55:04Z | |
| dc.date.available | 2023-03-24T18:55:04Z | |
| dc.date.issued | 2023-03-24 | |
| dc.description.abstract | This dissertation brings together a variety of tools to investigate the processes that occur within the plumbing of mafic volcanic systems. In Chapter II we use a combined isotope, trace element, and thermal modeling approach to investigate the production of rhyolitic magmas at the active Krafla Volcano in Iceland which lies directly on the Mid-Atlantic Ridge. There we found evidence for differentiation of basalts to rhyolites through a combined partial melting of the hydrated basaltic crust followed by subsequent late-stage fractional crystallization to produce highly evolved rhyolitic magmas. In Chapter III we turn our attention to a larger extinct mafic system, the Columbia River Flood Basalts. In this chapter we compile a database of whole-rock geochemical data sampled from the CRB and use both supervised and unsupervised machine learning to quantify and interpret groupings and variation in the dataset. We evaluate the relationships between the known stratigraphic groups and then build a classification model that quantitatively recognizes the chemical variation that defines the existing stratigraphic groups. We find that the geochemical variation and relationships within the stratigraphy are indicative of common processes of recharge, assimilation and fractional crystallization. In Chapter IV we apply this stratigraphic model to sort unknown samples of intrusive dike whole rock geochemistry into the CRB stratigraphy. These samples from the Wallowa Mtns and specifically from the Maxwell Lake area, provide further insight into the plumbing system of the CRB using a combination of field methods, machine learning, and comparison to other studies to investigate variation along strike within a dike complex. Along with the results presented in the chapter, we also include the full dataset of dike geochemical data and the probability distributions for the classification of these dike samples using both 70% and 100% of the training set (Supplemental Files S1, S2 and S3). In these three chapters we both find new evidence for processes occurring in these mafic systems and show the efficacy of these machine learning techniques when applied to whole rock geochemical data from volcanic systems. This dissertation includes previously published coauthored material. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/28080 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.subject | large igneous province | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Magma Chamber | en_US |
| dc.title | From Isotopes and Whole Rock Geochemistry to Machine Learning: Diving into the Plumbing System of Large Mafic Eruptions using a Diverse Geochemical Toolset to Investigate Magmatic Processes | |
| dc.type | Electronic Thesis or Dissertation | |
| thesis.degree.discipline | Department of Geological Sciences | |
| thesis.degree.grantor | University of Oregon | |
| thesis.degree.level | doctoral | |
| thesis.degree.name | Ph.D. |
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