We investigate the preparatory phase of the 2009, Mw 6.1 L’Aquila earthquake in central Italy by analyzing the temporal evolution and spatial distribution of the seismic moment, M0, to radiated energy, ES. Our approach focuses on monitoring the deviations of the scaling between M0 and ES with respect to a model calibrated for the background seismicity. The temporal evolution of these deviations, defined as Energy Index (EI), identifies the onset of the activation phase 1 week before the mainshock. We show that foreshocks are characterized by a progressive increase in slip per unit stress, in agreement with the diffusion of highly pressurized fluids before the L’Aquila earthquake proposed by previous studies. Our results suggest that the largest events occur where energy index (EI) is highest, in agreement with the existing link between energy index (EI) and the mean loading stress.

Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.

Seismic sequences are a powerful tool to locally infer geometrical and mechanical properties of faults and fault systems. In this study, we provided detailed location and characterization of events of the 3–7 July 2020 Irpinia sequence (southern Italy) that occurred at the northern tip of the main segment that ruptured during the 1980 Irpinia earthquake. Using an autocorrelation technique, we detected more than 340 events within the sequence, with local magnitude ranging between −0.5 and 3.0. We thus provided double difference locations, source parameter estimation, and focal mechanisms determination for the largest quality events. We found that the sequence ruptured an asperity with a size of about 800 m, along a fault structure having a strike compatible with the one of the main segments of the 1980 Irpinia earthquake, and a dip of 50–55° at depth of 10.5–12 km and 60–65° at shallower depths (7.5–9 km). Low stress drop release (average of 0.64 MPa) indicates a fluid-driven initiation mechanism of the sequence. We also evaluated the performance of the earthquake early warning systems running in real-time during the sequence, retrieving a minimum size for the blind zone in the area of about 15 km.

<p>Including site specific amplification factors in ground motion prediction models represented an advance for PSHA (Atkinson 2006; Rodríguez-Marek et al. 2013; Kotha et al. 2017) that has become nowadays a standard. However, this issue has only recently received attention by the seismological community of earthquake early warning (EEW) (Spallarossa et al., 2019; Zhao and Zhao, 2019), which applications require a real-time prediction of ground motion and the delivery of alert messages to users for mitigating their exposure to seismic risk. Indeed, all EEW systems are high-technological infrastructures devoted to the real-time and automatic detection of earthquakes, rapid assessment of the associated seismic hazard for targets and the prompt delivery of alerts trough fast telecommunication networks. Among them, the on-site approaches are based on seismic networks placed near to the target, indifferently by the location of seismic threats and they issue the alert predicting the ground motion at the target from P-wave parameter. This configuration cause that On-Site EEWS are generally highly affected by site conditions.</p><p>In this work, we calibrated ground motion prediction models for on-site EEW considering acceleration response spectra (RSA) and the P-waves EEW parameters Iv2 and Pd, and we investigated the role of site-effects. We considered a dataset of nearly 60 earthquakes belonging to the Central Italy 2016-17 sequence. The high density of stations near to the sequence has allowed us to use a non-ergodic random-effect regression approach to explore and to reduce the contribution of site-effects to the uncertainty of the On-site laws predictions. We grouped the records in two ways: by stations and by EC8 classification. Then, we validated the estimated models by the Leave One Out (L1Out) technique and applied a K-means analysis to assess the performance of the EC8 classification.</p><p>The residuals analysis proved that grouping by station provides a set of relations that improves the predictions at many stations. On the contrary, L1Out cross-validation proved that the regressions retrieved grouping by EC8 classification produce higher uncertainties on the predictions than the others. Furthermore, the cross-validation proved that Iv2 is more correlated to RSA than Pd. Finally, the analysis of the random effect vs period curves confirmed that EC8 classification is unrelated to the site effect on RSA even looking only at the trend of these curves.</p><p>In conclusion, non-ergodic random-effect regression can be used also in the EEW applications to predict site-specific ground motion. EEWS that use this approach are less dependent by site-effect and able to provide more precise and reliable alerts.</p>

Investigating the strength of faults is a fundamental goal in seismology. Key open scientific questions on faults interaction and on the nucleation and preparation phase of large earthquakes are, in the final instance, all related to crustal and fault strength. However, as observed by Ben-Zion (2019), our understanding of processes occurring in the crust is inevitably hampered by the impossibility of collecting measurements directly within the seismogenic volumes.A better understanding of the in situ crustal strength cannot be achieved by studying large earthquakes only, as they are rare and provide information only on the final stages of the processes that lead to the rupture. Thus, the scientific community is mining data across wider spatiotemporal scales, both in the laboratory, by observing