Modeling volcanic unrest by data assimilation

Volcanic activity may lead to potential volcanic eruptions, but it also provides critical information for understanding the physical processes within a volcanic system. Combining multiple observations and advanced physical models allows us to explore the response of the surrounding host rock to changes in the physical condition in a magmatic system. This work focuses on developing and applying a robust data-model fusion framework to investigate the mechanisms involved in volcanic unrest, such as deformation, failure, and pore fluid migration. First, using a series of tests based on the synthetic data, I optimize a data assimilation technique, Ensemble Kalman Filter (EnKF), to improve its performance in forecasting volcanic unrests with multiple geodetic observations. Then, the robustness of the EnKF is confirmed in application to the unrest and 2009 eruption of Kerinci volcano, Indonesia. To understand the effects of uncertain rheology on our model results, I conduct a systematic sensitivity study to determine the impact of rheology on the host-rock failure prediction. With a better understanding of the uncertainties in my models, I establish numerical models by integrating multiple observations to investigate the magma reservoir dynamics, crustal stress, failure-related seismicity, and hydrological interactions of two different magmatic systems, Laguna del Maule in the Andes, Chile, and Atka in the Aleutian, USA. In both systems, the pre-existing structures and pore fluids play critical roles in catalyzing seismicity, redistributing masses, and delaying/trigger eruptions.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Geology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Zhan, Yan

Contributors dc:contributor

• Gregg, Patricia M
• Liu, Lijun
• Marshak, Stephen
• Lundstrom, Craig

• volcano
• data assimilation
• numerical modeling
• deformation
• stress
• failure

Statement dc:rights

• Copyright 2020 Yan Zhan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108462

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108462