Placeholder for sequential data assimilation framework for volcanic unrest.
2026
EnKF-based Eruption Forecasting Framework
This project develops an ensemble Kalman filter framework to assimilate geodetic observations into thermo-mechanical models of volcanic reservoirs.
Background
Volcanic unrest evolves through time, requiring models that update probabilistically as new observations become available.
Research Question
Can sequential data assimilation improve forecasting of magma reservoir pressurization and failure?
Methods
The framework assimilates InSAR, GNSS, and model outputs using ensemble-based forecasting.
Key Findings
The goal is to distinguish benign inflation, failed intrusions, and potentially eruptible conditions.