Building a Better Forecast: Reformulating the Ensemble Kalman Filter for Improved Applications to Volcano...

Area of Science:

• Volcanology
• Geophysics
• Data Assimilation

Background:

• Increasing data from monitored volcanoes necessitates automated methods for modeling magma systems.
• The Ensemble Kalman Filter (EnKF) is an inversion technique used for volcanic unrest forecasting, correlating geodetic data with magma reservoir stresses.

Purpose of the Study:

• To test and identify optimal Ensemble Kalman Filter (EnKF) configurations for volcanological applications.
• To address limitations in resolving magmatic conditions due to similar surface expressions of reservoir changes.

Main Methods:

• Generated synthetic geodetic deformation data under controlled conditions.
• Assimilated synthetic data using various published Ensemble Kalman Filter (EnKF) implementations.
• Evaluated the performance of different EnKF variants for magma system modeling.

Main Results:

• Many EnKF variants commonly used in other fields underperform in volcanological forecasting.
• Parameter correlations within the EnKF's Monte Carlo ensemble distort model state updates.
• The filter systematically favors certain parameter changes, leading to partially inaccurate solutions.

Conclusions:

• Standard EnKF workflows require optimization for accurate volcanic magma system modeling.
• Interrupting parameter correlations can mitigate some inaccuracies.
• Further research is needed to develop novel approaches for optimizing EnKF in volcanology.