Accurate forecasting eruptive activity is a core challenge in volcanology. Here, we describe an end of eruption forecast during the 2021 La Palma eruption using continuous GNSS geodetic data. We observed that co-eruptive deflation resembled a quasi-exponential trend and used it to forecast when deflation would cease equating to when the eruption would end. The forecast was done within the eruption, however was not operational due to large uncertainty in the unknown pressure dropt threshold needed to stop magma upflow. In hindcast, we explore minimum datasets needed and how forecast uncertainty reduces with increase in ingested data. We conclude that forecasts could be possible after the time-scale around the e-folding time of the exponential decay and quite accurate ones after twice that time-scale. Our results also indicate that the eruption was controlled by the dynamics of a Moho depth reservoir beneath Cumbre Vieja volcano.

Improved imaging of the spatio-temporal growth of fault slip is crucial for understanding the driving mechanisms of earthquakes and faulting. This is especially critical to properly evaluate the evolution of seismic swarms and earthquake precursory phenomena. Fault slip inversion is an ill-posed problem and hence regularisation is required to obtain stable and interpretable solutions. An analysis of compiled finite fault slip models shows that slip distributions can be approximated with a generic elliptical shape, particularly well for M≤7.5 events. Therefore, we introduce a new physically-informed regularisation to constrain the spatial pattern of slip distribution. Our approach adapts a crack model derived from mechanical laboratory experiments and allows for complex slipping patterns by stacking multiple cracks. The new inversion method successfully recovered different simulated time-dependent patterns of slip propagation, i.e., crack-like and pulse-like ruptures, directly using wrapped satellite radar interferometry (InSAR) phase observations. We find that the new method reduces model parameter space, and favours simpler interpretable spatio-temporal fault slip distributions. We apply the proposed method to the 2011 March-September normal-faulting seismic swarm at Hawthorne (Nevada, USA), by computing ENVISAT and RADARSAT-2 interferograms to estimate the spatio-temporal evolution of fault slip distribution. The results show that (1) aseismic slip might play a significant role during the initial stage, and (2) this shallow seismic swarm had slip rates consistent with those of slow earthquake processes. The proposed method will be useful in retrieving time-dependent fault slip evolution and is expected to be widely applicable to studying fault mechanics, particularly in slow earthquakes.