Dissertation/Thesis Abstract

Identifying Strombolian Eruptions through Cross-Correlation of Seismic Data and Machine Learning of Infrared, Lava-Lake Images on Mount Erebus, Antarctica
by Dye, Brian Christopher, M.S., University of Louisiana at Lafayette, 2018, 112; 10815582
Abstract (Summary)

Mount Erebus, Antarctica, is a volcano with frequent lava-lake eruptions known as strombolian eruptions. The larger of these eruptions create strong seismic waves and have a characteristic seismic signature that can be analyzed through three-component cross-correlation to distinguish smaller strombolian eruptions from the background noise of the volcano. The addition of an infrared camera on the rim of Mount Erebus allows for the confirmation of strombolian eruptions as opposed to unrelated seismic activity. This research finds that eruption events can also be detected categorizing the images using machine learning. Machine learning in seismology is now a commonly used technique, yet to date, no research using machine learning has ever been used in volcanology. Image categorization along with cross-correlation can improve automatic detection of strombolian eruptions.

Indexing (document details)
Advisor: Morra, Gabriele
Commitee: Gottardi, Raphael, Natalia Sidorovskaia, Natalia A.
School: University of Louisiana at Lafayette
Department: Geology
School Location: United States -- Louisiana
Source: MAI 58/05M(E), Masters Abstracts International
Subjects: Computational physics, Geology, Geophysics, Artificial intelligence
Keywords: Antartica, Eruption, Machine learning, Mount Erebus, Strombolian eruption, Volcano
Publication Number: 10815582
ISBN: 978-1-392-04145-1
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