Physics-based eruption forecasting at Kīlauea volcano using an Ensemble Kalman Filter

Today, most forecasts of volcanic eruptions are based on expert opinion, making them fundamentally subjective. Such forecasts have often proven successful but have clear limitations. Novel quantitative forecasting techniques have shown promise in experimental settings (hindcasting) but face numerous operational challenges and most have rarely if ever been applied to real-world eruptions (forecasting).

In this talk I will discuss efforts to forecast a remarkable ongoing series of more than 40 high lava fountain eruptions at Kīlauea volcano, Hawaii, using a simple physics-based model in an Ensemble Kalman Filter (EnKF) data assimilation algorithm. Using this method, which is believed to be the first implementation of a physics-based EnKF eruption forecast, the times of Kīlauea’s lava fountain eruptions can be forecast days to weeks in advance. The method assimilates geodetic data to constrain the evolving state of the system, provides insight into the eruption mechanism and rate of magma supply to the volcano, and produces fully probabilistic forecasts. These forecasts are combined with other information, including forecasts based on machine learning algorithms, to derive forecast windows, which are disseminated to the public and to partner agencies for hazards mitigation activities. In this way, novel eruption forecasting tools are continually developed which serve an important public need while also improving understanding of the volcanic system.