Foreshocks are among the most powerful tools to study the processes that occur before main earthquakes. However, their detection is still sparse, especially for relatively small earthquakes. We present here a detailed foreshock analysis for the 2019 Balsorano (Italy) earthquake (Mw4.4). To improve the detection before and after the mainshock, we use receivers at distances of <75 km from the targeted seismicity, through template matching. To improve the understanding of the mechanism(s) behind the earthquake initiation, we detail the evolution of the sequence associated to this earthquake, using waveform clustering and hypocenter relocation. Differences between foreshocks and aftershocks are revealed by this analysis. Moreover, distinct patterns associated to the different seismic activities are revealed. The observed behavior highlights a complex initiation process, which apparently starts on an adjacent antithetic fault. Finally, the aftershock activity comprises different clusters with distinct spatio-temporal patterns, which suggests that each cluster has distinct triggering mechanism(s).

We apply unsupervised machine learning to three years of continuous seismic data to unravel the evolution of seismic wavefield properties in the period of the 2009 L’Aquila earthquake. To obtain sensible representations of wavefield properties variations, wavefield features, i.e. entropy, coherency, eigenvalue variance, and first eigenvalue, are extracted from the covariance matrix analysis of continuous array seismic data. The defined wavefield features are insensitive to site-dependent local noise, and can inform the spatiotemporal properties of seismic waves generated by sources inside the array. We perform a sensitivity analysis of these wavefield features and build unsupervised learning based on the uncorrelated features to track the evolution of source properties. By clustering the wavefield features, our unsupervised analysis avoids explicit physical modeling (e.g. location of events, magnitude estimation) and can naturally separate peculiar patterns solely from continuous seismic data. The unsupervised learning of wavefield features reveals distinct clusters well correlated with different periods of the seismic cycle.

How faulting processes lead to a large earthquake is a fundamental question in seismology. To better constrain this pre-seismic stage, we create a dense seismic catalog via template matching to analyze the precursory phase of the Mw 6.3 L’Aquila earthquake that occurred in central Italy in 2009. We estimate several physical parameters in time, such as the coefficient of variation, the seismic moment release, the effective stress drop, and analyze spatio-temporal patterns to study the evolution of the sequence and the earthquake interactions. We observe that the precursory phase experiences multiple accelerations of the seismicity rate that we divide into two main sequences with different signatures and features: the first part exhibits weak earthquake interactions, quasi-continuous moment release, slow spatial migration patterns, and a lower effective stress drop, pointing to aseismic processes. The second sequence exhibits strong temporal clustering, rapid spatial expansion of the seismicity and larger effective stress drop typical of a stress transfer process. We interpret the differences in the seismicity behavior between the two sequences as distinct physical mechanisms that are controlled by different physical properties of the fault system. We conclude that the L’Aquila earthquake is preceded by a complex preparation, made up of different physical processes taking place over different time scales on faults with different physical properties.

The Alto Tiberina Fault system located in the Northern Apennines (Italy) consist of a low angle normal fault which radiates micro-seismicity on a constant rate and is assumed to host continuous creep. In the hanging wall, on top of the low angle normal fault, a network of syn- and antithetic high angle faults frequently hosts swarm seismic sequences, one of which has been associated to a transient aseismic deformation signal. To study in detail the dynamics related to the seismic and its relationships with aseismic deformation processes occurring in this fault system, we apply template matching on seismic data recorded at an array of borehole stations, to derive a high-resolution earthquake catalog. We then quantify the time and space clustering of the seismicity which reveals elevated clustering within the hanging wall during an aseismic deformation period. This reflects the complex evolution of aseismic slip together with the complexity of the shallow fault system. On the other hand, we observe a bimodal seismicity along the low angle normal fault, with continuous and diffuse seismicity, and rapid bursts which could suggest rapid fluid releases. Along the low angle normal fault, we additionally identify repeating earthquakes and model them assuming a creep model. Our results open the possibility for other models than the actual model of inter-seismic deformation to explain the seismicity along the Alto Tiberina low angle normal fault.

We study a large and long-lived earthquakes swarm occurring in 2020-2021 in the Bransfield Basin, south of the South Shetland islands, Antarctica. We make use of local seismological stations to detect and characterize more than 36000 small earthquakes, occurring from the end of August 2020 to June 2021. Together with the occurrence of the ~36000 earthquakes, we observe a significant (up to 8cm) geodetic deformation at nearby GPS stations. By joint interpretation of b-value, spatiotemporal evolution of seismicity and geodetic deformation, we infer a volcanic origin for this swarm, which takes place close to the ridge axis. Our study suggest that a significant amount of extension observed at the Bransfield Basin ridge is occurring in rapid deformation episodes (e.g., 1 year), and is most likely driven by volcanic activity localized at the ridge axial volcanic structure, rather than at the rifting bounding border faults.

Most of the largest volcanic activity in the world occurs in remote places as deep oceans or poorly monitored oceanic islands. Thus, our capacity of monitoring volcanoes is limited to remote sensing and global geophysical observations. However, the rapid estimation of volcanic eruption parameters is needed for scientific understanding of the eruptive process and rapid hazard estimation. We present a method to rapidly identify large volcanic explosions, based on analysis of seismic data. With this methodology, we promptly detect the January 15, 2022 Hunga Tonga eruption. We then analyze the seismic waves generate by the volcanic explosion and estimate important first-order parameters of the eruption. We further relate the parameters with the volcanic explosivity index (VEI). Our estimate of VEI~6, indicate how the Hunga Tonga eruption is among the largest volcanic activity ever recorded with modern geophysical instrumentation, and can provide new insights about the physics of large volcanoes.