Building a data assimilation framework for forecasting volcanic activity during periods of unrest

Patricia Gregg, University of Illinois at Urbana-Champaign

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A primary motivation for investigating volcanic systems is developing the ability to predict eruptions and mitigate disaster for vulnerable populations. Significant effort has been spent to increase monitoring and data collection campaigns to mitigate potential volcano disasters. To take the next step towards the goal of forecasting eruptions, robust model-data fusion techniques must be developed to tackle the problem of combining multiple, disparate volcano monitoring datasets with increasingly sophisticated numerical models. I propose to utilize the Ensemble Kalman Filter (EnKF) sequential data assimilation framework to investigate the precursors leading to volcanic eruption and develop a volcano forecasting methodology. These proposed advancements build on my previous efforts, which have applied the EnKF volcano forecasting approach in pseudo-4D with synthetic data sets. Progress towards a full 4D implementation has been impeded due to the computational needs of the Markov Chain Monte Carlo and finite element approaches and the storage requirements for sizable volcano monitoring datasets. Blue Waters is uniquely positioned to support this investigation due to its speed, computational scale, and its ability to handle large datasets with the necessary data bandwidth for online assimilation into finite element models. A successful implementation of this work will provide multiple breakthroughs for the field of volcano hazards, and most importantly, a framework for forecasting volcanic unrest.