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Ensemble based data assimilation approaches, such as the Ensemble Kalman Filter (EnKF), have been widely and successfully implemented to combine observations with dynamic forecast models. In this study the EnKF is adapted to assimilate ground deformation observations from interferometric synthetic-aperture radar (InSAR) and GPS into thermomechanical finite element models (FEM) to evaluate volcanic unrest. Two eruption hindcasts are investigated: the 2008 eruption of Okmok volcano, Alaska and the 2018 eruption of Sierra Negra volcano, Galapagos, Ecuador. At Okmok, EnKF forecasts tensile failure and the lateral movement of the magma from a central pressure source in the lead up to its 2008 eruption indicating potential for diking. Alternatively, at Sierra Negra, the EnKF forecasts significant shear failure coincident with a Mw 5.4 earthquake that preceded the 2018 eruption. These successful hindcasts highlight the flexibility and potential of the volcano EnKF approach for near real time monitoring and hazard assessment at active volcanoes worldwide.

In this study, synthetic numerical experiments are conducted to investigate how well the Ensemble Kalman Filter (EnKF) data assimilation approach distinguishes between two potential drivers of ground deformation at volcanic systems: pressurization and lateral reservoir expansion. Numerical models indicate that pressure-driven inflation creates larger radial displacements relative to inflation driven by lateral expansion. However, the introduction of noise can obscure these differences in simulated geodetic data. Although the EnKF does not fully reproduce the original synthetic models, it remains sensitive to changes in the magma reservoir's aspect ratio and is able to distinguish between the two inflation mechanisms. Ultimately, there remains significant non-uniqueness in how changes in reservoir pressure and size are reflected in surface deformation for any given aspect ratio, but future innovations may continue to improve filter performance.