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The Community Stress Drop Validation Study has been organized as a technical activity group (TAG) of SCEC (Southern California Earthquake Center) with the aim of investigating the source parameters of the 2019 Ridgecrest seismic sequence in California. Information about the stress drop TAG are available trough the benchmark web-page (https://www.scec.org/research/stress-drop-validation). Several groups applied different techniques to a shared data set with the objective of extracting source parameters (e.g. seismic moment and corner frequency) and in turn to estimate the stress drop. We applied a spectral decomposition approach known as generalized inversion technique (GIT) and the overall analyses are presented in a series of two articles (Bindi et al 2023a; Bindi et al 2023b). Results in the form of files, figures, and tables are disseminated through this archive.

Near-fault observations can provide insights into the physical process interaction between fault slip activation, fluid presence/migration and seismicity production, processes acting at different timescales that generate large earthquakes.The DETECT experiment aims at exploiting very dense seismic networks deployed across a segmented faults system to foster the development of scientific integrated methodologies for monitoring and imaging the faults behavior during the inter-seismic phase. Target of the monitoring is to: detect and track space-time trends of different source parameters that could be related to a preparation process leading to a larger earthquake; investigate the frictional and stress states of the fault segments to anticipate the characteristics of the future large earthquake (e.g., hypocenter but also future large seismic energy release locations); analyze the interactions between the different fault segments to model/anticipate potential cascade effects.The DETECT experiment is carried out in the Irpinia area (southern Italy), one of the regions in Italy and Europe showing the highest seismic hazard. Since august 2021, a constellation of 20 seismic arrays, for a total of 200 seismic stations (20 broad-band sensors and 180 short-periods), has been installed over the fault segments responsible for the Ms 6.9, 1980 Irpinia earthquake, the strongest and most destructive seismic event of the last half-century in southern Italy.DETECT results from a joint effort of local Universities, National and International Research Institutes. A novel and crucial aspect is that, differently from most studies concerning intra-plate earthquakes which are usually carried out after that large magnitude earthquakes have occurred, DETECT aims to put us in advantageous position and to unveil the preparatory process that generate large earthquakes and anticipate the role of the segmentation by studying at one time different fault segments, some of which are in rather late stage of their seismic cycle.With this contribution, we aim to present the DETECT experiment, the preliminary results and foster additional cooperation including complementary expertise to further enrich the partnership.

We describe the main structure and outcomes of the new probabilistic seismic hazard model for Italy, MPS19 [Modello di Pericolosità Sismica, 2019]. Besides to outline the probabilistic framework adopted, the multitude of new data that have been made available after the preparation of the previous MPS04, and the set of earthquake rate and ground motion models used, we give particular emphasis to the main novelties of the modeling and the MPS19 outcomes. Specifically, we (i) introduce a novel approach to estimate and to visualize the epistemic uncertainty over the whole country; (ii) assign weights to each model components (earthquake rate and ground motion models) according to a quantitative testing phase and structured experts’ elicitation sessions; (iii) test (retrospectively) the MPS19 outcomes with the horizontal peak ground acceleration observed in the last decades, and the macroseismic intensities of the last centuries; (iv) introduce a pioneering approach to build MPS19_cluster, which accounts for the effect of earthquakes that have been removed by declustering. Finally, to make the interpretation of MPS19 outcomes easier for a wide range of possible stakeholders, we represent the final result also in terms of probability to exceed 0.15 g in 50 years., peer-reviewed

Coda-Q is used to estimate the attenuation and scattering properties of the Earth. So far focus has been on earthquake data at frequencies above 1 Hz, as the high noise level in the first and second microseismic peak, and possibly lower scattering coefficient, hinder stable measurements at lower frequencies. In this work, we measure and map coda-Q in the period bands 2.5-5 s, 5-10 s and 10-20 s in the greater Alpine region using noise cross-correlations between station pairs, based on data from permanent seismic stations and from the temporary AlpArray experiment. The observed coda-Q for short interstation distances is independent of azimuth so there is no indication of influence of the directivity of the incoming noise field on our measurements. In the 2.5-5 s and 5-10 s period bands, our measurements are self-consistent, and we observe stable geographic patterns of low and high coda-Q in the period bands 2.5-5 s and 5-10 s. In the period band 10-20 s, the dispersion of our measurements increases and geographic patterns become speculative. The coda-Q maps show that major features are observed with high resolution, with a very good geographical resolution of for example low coda-Q in the Po Plain. There is a sharp contrast between the Po Plain and the Alps and Apennines where coda-Q is high, with the exception a small area in the Swiss Alps which may be contaminated by the low coda-Q of the Po Plain. The coda of the correlations is too short to make independent measurements at different times within the coda, so we cannot distinguish between intrinsic and scattering Q. Measurements on more severely selected data sets and longer time-series result in identical geographical patterns but lower numerical values. Therefore, high coda-Q values may be overestimated, but the geographic distribution between high and low coda-Q areas is respected. Our results demonstrate that noise correlations are a promising tool for extending coda-Q measurements to frequencies lower than those analysed with earthquake data.

Onsite earthquake early warning (EEW) systems exploit predictive models relating fea- tures extracted over the P-wave window to S-wave ground-motion parameters. These models are usually calibrated considering recordings from multiple stations and combining datasets from different regions under the ergodic assumption. Here, we show that the local-site conditions can play a significant role in determining the performance of onsite EEW predictive models in terms of rates of false or missed alerts. Interestingly, if partially nonergodic models are implemented, as done in probabilistic seismic hazard analysis, the negative impact of local-site amplifications can be mitigated. We explore the influence of site effects for onsite EEW predictive models calibrated between the peak displacement (Pd ) and integral squared velocity (Iv2) measured over a 3 s P-wave window, and the accel- eration response spectra (RSA) at nine different periods T (T 0:1, 0.15, 0.2, 0.3, 0.5, 0.75, 1.0, 1.5, and 2.0 s). We consider 58 earthquakes with magnitudes between Mw 3.7 and 6.5, belonging to the 2016–2017 central Italy seismic sequence that have been recorded by 100 accelerometer stations at hypocentral distances lesser than 150 km. We implement a mixed-effects regression analysis to explore the variability of the ground motion in terms of RSA predicted at different sites by considering two different group levels: in the first, each station is considered separately; in the second, we consider the Eurocode 8 (EC8, 2004) soil classification. Considering a probabilistic alert decision module applied to data from two selected stations, we show that the predictive models including site effects provide more reliable alerts, reducing the false alarms from 2.6% to 0.53% and the missed alarms from 10.1% to 4.8%. The residuals analysis shows that including a site-specific random effect in the predictive model contributes to reducing the apparent aleatory variability, whereas grouping data by EC8 classification does not provide significant benefit for EEW purposes.

In this study, we analyzed 10 yrs of seismicity in central Italy from 2008 to 2017, a period witnessing more than 1400 earthquakes in the magnitude range 2.5≤Mw≤6.5⁠. The data set includes the main sequences that have occurred in the area, including those associated with the 2009 Mw 6.3 L'Aquila earthquake and the 2016–2017 sequence (⁠Mw 6.2 Amatrice, Mw 6.1 Visso, and Mw 6.5 Norcia earthquakes). We calibrated a local magnitude scale, investigating the impact of changing the reference distance at which the nonparametric attenuation is tied to the zero‐magnitude attenuation function for southern California. We also developed an attenuation model to compute the radiated seismic energy (⁠Es⁠) from the time integral of the squared ground‐motion velocity. Seismic moment (⁠M0⁠) and stress drop (⁠Δσ⁠) were estimated for each earthquake by fitting a ω‐square model to the source spectra obtained by applying a nonparametric spectral inversion. The Δσ‐values vary over three orders of magnitude from about 0.1 to 10 MPa, the larger values associated with the mainshocks. The Δσ‐values describe a lognormal distribution with mean and standard deviation equal to log(Δσ)=(−0.25±0.45) (i.e., the mean Δσ is 0.57 MPa, with a 95% confidence interval from 0.08 to 4.79 MPa). The Δσ variability introduces a spread in the distribution of seismic energy versus moment, with differences in energy up two orders of magnitudes for earthquakes with the same moment. The variability in the high‐frequency spectral levels is captured by the local magnitude (⁠ML⁠), which scales with radiated energy as ML=(−1.59+0.52logEs) for logEs≤10.26 and ML=(−1.38+0.50logEs) otherwise. As the peak ground velocity increases with increasing Δσ⁠, local and energy magnitudes perform better than moment magnitude as predictors for the shaking potential. The availability of different magnitude scales and source parameters for a large earthquake population will help characterize the between‐event ground‐motion variability in central Italy.

The zonation presented in this study has been developed with the aim of applying it as a branch of the logic tree that will be used for the new Italian seismic hazard map, presently in preparation according to the approach of seismotectonic probabilism. With respect to the zonation used for the present official seismic hazard map of Italy, the zonation proposed here considers narrower sources and is based on new and updated seismological data. In particular, some new seismogenic zones are proposed here, introducing areas that were not considered seismogenic until now (e.g., the narrow sources characterised by the presence of transform faults which are almost normal to the trend of the northern Apennines). The preliminary seismic hazard estimates produced with this new zonation aim to identify possible problems that the zonation introduces in the seismicity characterization of the seismogenic zones. As the present seismic hazard assessment was computed by considering a different attenuation model with respect to the one applied for the previous national seismic hazard maps, a re-elaboration of the most recent map referring to Italy has been developed: the comparison of the two maps is a good indicator of the areas where additional seismological investigation is needed to support the zonation presented here. In particular, some zones are not adequately documented with regard to seismicity and a different computation of the seismicity rates is suggested.

The automatic processing of seismic data, whether for real-time seismic warning system or to reprocessing large amount of seismic recordings, is increasingly being demanded by seismologists especially in case of emergency as for the Emilia sequence in may-june 2012. In this study is presented a comparison between the AutoPicker (DipTeRiS, University of Genova) a new method used for automatic accurate onset phase picking for both P and S wave arrival based on the Akaike's information criterion (AIC), a solid and tested picker as the STA/LTA in Antelope software and the manual pickings. In order to construct the database used for the relocation of Emilia sequence, the RAN strong motion database has been merged with the available velocity and acceleration data extracted from the EIDA database (European Integrated Data Archive) and velocity data recorded by the Southeastern Alps Integrated Network (DMG, OGS, ARSO and ZAMG). The fault system of the Emilia earthquake area is complex and it is not easy to assess which fault has moved. A precise localization of the sequence is essential. The manual pickings, the equivalent locations and the choice of the most appropriate velocity model (“Iside”) used in this study are the results of a work done in collaboration with Università di Chieti and DPC, not described here. The main problem of the AutoPicker and Antelope software is to discriminate events that occur very close to each other in time. The best way to solve that issue is choosing the best setup of both techniques to minimize the problem. Then we are implementing the AutoPicker technique on the Antelope system routinely used by DMG for the real-time data analysis. In addition we use automatic locations and manual locations to produce a travel time tomography of the field near the generating fault of Emilia sequence 2012 and compare the different results in order to study how much the location's errors influence the tomography's results.