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Volcanic eruptions pose a significant and sometimes unpredictable hazard, especially at systems that display little to no precursory signals. For example, the 2008 eruption of Okmok volcano in Alaska notably lacked observable short-term precursors despite years of low-level unrest. This unpredictability highlights that direct monitoring alone is not always enough to reliably forecast eruptions. In this study, we use the Ensemble Kalman Filter (EnKF) to produce a successful hindcast of the Okmok magma system in the lead up to its 2008 eruption. By assimilating geodetic observations of ground deformation, finite element models track the evolving stress state of the magma system and evaluate its stability using mechanical failure criteria. The hindcast successfully indicates an increased eruption likelihood due to tensile failure weeks in advance of the 2008 eruption. The effectiveness of this hindcast illustrates that EnKF-based forecasting methods may provide critical information on eruption probability in systems lacking obvious precursors.

Plain Language Summary Volcano monitoring agencies routinely use increases in volcanic unrest as indicators of the potential for eruption. However, for some eruptions, such as the 2008 eruption of Okmok volcano in Alaska, these behaviors can be subtle or missing altogether. In this study, a new statistics-based volcano forecasting approach is used to test whether computer models are able to capture an increase in eruption likelihood leading up to the 2008 event. The models indicate that Okmok was trending toward eruption weeks in advance due to the increased probability of failure of the magma chamber. This successful test indicates that stress around the magma chamber is a strong predictor of volcano stability and that this method could apply to active volcanic systems and improve hazard mitigation efforts.