Physics & Astronomy ETDs

Publication Date

Spring 5-14-2022

Abstract

To better understand volcanoes and their processes is important from both a fundamental science perspective and for hazard monitoring purposes. The complexity and limitations we face in pursuing such a science are numerous and this dissertation explores how an interdisciplinary approach combining physics, computer science, and volcanology can address this complexity in a straightforward and meaningful way. This is achieved through various modelling techniques across three studies: (1) a first-order analytic modelling of stratovolcano topographic shape, (2) the use of a Bayesian joint inversion on gravity and novel cosmic-ray muon measurements for imaging flat-lying subsurface density anomalies, and (3) the use of a machine learning model for subsurface density prediction at a volcano. All three studies presented provide important insights into understanding what goes on underneath volcanoes, with the intention that they might guide future hazard monitoring practices.

Degree Name

Physics

Level of Degree

Doctoral

Department Name

Physics & Astronomy

First Committee Member (Chair)

Mousumi Roy

Second Committee Member

Richard Rand

Third Committee Member

Keith Lidke

Fourth Committee Member

Brandon Schmandt

Language

English

Keywords

volcano physics, subsurface imaging, machine learning, bayesian joint inversion

Document Type

Dissertation

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