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Forecasting and monitoring submarine volcanic eruptions represent a particular challenge due to the lack of direct surface observations. In the present study, we investigate the dynamics of the 2011 El Hierro submarine eruption using phase autocorrelation and cross-correlation from 2 years of continuous seismic records. Time evolution analysis of the waveform similarity allows us to clearly identify different preeruptive phases of this new volcano, as well as three magmatic intrusions that occurred in 2012. We use probabilistic sensitivity kernels to locate the places of the highest dynamics within the magmatic accumulation zone and the extinct volcanic area of Tinor that might have acted as stress barriers, guiding the magma from the North of El Hierro Island to the final eruption site at the South. Our results highlight the potential of ambient noise methods to monitor volcanic hazard and unrest even with sparse data sets and limited knowledge of the region.

Plain Language Summary In 2011, a new volcano in the Canary Archipelago emerged at the southern edge of El Hierro Island after the archipelago had shown 40 years of quiescence. The volcanic eruption was submarine and unexpected, which impeded setting up instruments around the volcanic edifice and far in advance. This limited spatial distribution of instruments and the scarce data set of observables represent a particular challenge in forecasting and monitoring submarine volcanoes and motivated our research. We analyze the volcanic activity using 2 years of continuous seismic records from El Hierro Island. Our approaches involve methods from seismic interferometry as autocorrelation and cross correlation to determine waveform changes related to the different phases of volcanic activity and probabilistic sensitivity kernels of wave propagation to locate the places of the highest dynamics within the magmatic accumulation zone. Our results highlight the potential of ambient noise methods to forecast and monitor volcanic hazard and unrest even with sparse data sets in less known regions.