Forecasting volcanic unrest through geodetic data assimilation

Albright, Jack

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https://hdl.handle.net/2142/116212 Copy

Description

Title

Forecasting volcanic unrest through geodetic data assimilation

Author(s)

Albright, Jack

Issue Date

2022-07-12

Director of Research (if dissertation) or Advisor (if thesis)

Gregg, Patricia M

Doctoral Committee Chair(s)

Gregg, Patricia M

Committee Member(s)

• Liu, Lijun
• Best, James
• Marshak, Stephen
• Pettijohn, Justin C

Department of Study

Geology

Discipline

Geology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• geodesy
• EnKF
• geodynamics
• eruption forecasting

Abstract

Volcanic eruptions pose a serious hazard to communities around the world, and one of the key goals of volcanology as a discipline is to better forecast volcanic unrest so that the damage and loss of life caused by future events can be minimized. To that end, many active volcanoes host extensive monitoring networks, both ground-based and spaceborne, that detect deviations from the system’s baseline behavior. This dissertation focuses on using the Ensemble Kalman Filter (EnKF), an advanced data assimilation technique, to derive the physical conditions of an active magma reservoir from geodetic measurements of ground deformation. The models produced by the EnKF can in turn be used to measure the stress state in and around the reservoir, determining its long-term mechanical stability and the likelihood of a physically triggered eruption. After successfully applying this framework to hind-cast the 2008 eruption of Okmok, Alaska, I use a series of synthetic tests to measure the EnKF’s sensitivity to different drivers of magmatic inflation. While changes in different reservoir parameters can produce very similar geodetic signals, the EnKF can broadly distinguish between different scenarios, albeit with some difficulty resolving exact reservoir parameters. By evaluating the performance of different variations on the EnKF workflow, I show that these distortions are persistent and arise from non-uniqueness in the geodetic observations. Although the application of geodetic data can only constrain a magma reservoir to within a range of non-unique states, that range is narrow enough to provide meaningful information about the system’s stability. In the end, I show that the EnKF can quickly and efficiently invert geodetic data, particularly high-resolution satellite measurements, to provide useful eruption forecasts.

Graduation Semester

2022-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/116212

Copyright and License Information

Copyright 2022 Jack Albright

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