Di Giovambattista, Rita

We investigate the variability of Brune stress drop (Δσ), apparent stress (τa), and Savage– Wood radiation efficiency (ηsw   τa= Δσ), in the 2013–2014 Mw 5.0 earthquake sequence that struck the Matese area in the southern Apennines range of Italy. The sequence is clustered in a relatively small crustal volume in the 13–22 km depth range, which is greater than that of background seismicity and normal-faulting sequences that occurred under the range axis, usually located in the first 15 km of the crust.We find high Savage– Wood radiation efficiency values for most of the analyzed earthquakes located in a narrow crustal volume, with values ranging from well above the self-similarity value to very high values as high as 0.55. In addition, a large variability in radiation efficiency (up to 90%) is observed for two similar magnitude events at different depths. Previous studies reported seismic evidence of fluid involvement in the nucleation process of the Matese earthquakes. By integrating our results with crustal geophysical data published recently, we propose that most of the earthquakes characterized by high values of ηsw are nucleated within high pore pressure zones located in the crystalline midcrust of Adria. We reckon that high pore pressure fluids of deep origin played a role in the rupture process and were responsible for themixed shear-tensile sources inferred from the analysis of the S-wave/P-wave spectral amplitude ratio for most of 2013–2014 earthquakes.

We present a new approach to estimate the predominant direction of rupture propagation during a seismic sequence. A fast estimation of the rupture propagation direction is essential to knowthe azimuthal distribution of shaking around the seismic source and the associated risks for the earthquake occurrence. The main advantage of the proposed method is that it is conceptually reliable, simple, and fast (near real time). The approach uses the empirical Green’s function technique and can be applied directly to the waveforms without requiring the deconvolution of the instrumental response and without knowing a priori the attenuation model and the orientation of the activated fault system. We apply the method to the 2016–2017 Amatrice-Visso-Norcia high-energy and long-lasting earthquake series in central Italy,which affected a large area up to 80 kmalong strike, withmore than 130,000 events of small-to-moderate magnitude recorded until the end of August 2022. Most of the selected events analyzed in this study have a magnitude greater than 4.4 and only four seismic events have a magnitude in the range of 3.3–3.7. Our results show that the complex activated normal fault system has a rupture direction mainly controlled by the pre-existing normal faults and by the orientation of the reactivated faults. In addition, the preferred direction of rupture propagation is also controlled by the presence of fluid in the pre-existing structural discontinuities. We discuss the possible role of fluids as a cause of bimaterial interface. Another important finding from our analysis is that the spatial evolution of seismicity is controlled by the directivity.

In this paper, we outline the scientific objectives, the experimental layout, and the collaborations envisaged for the GINGER (Gyroscopes IN GEneral Relativity) project. The GINGER project brings together different scientific disciplines aiming at building an array of Ring Laser Gyroscopes (RLGs), exploiting the Sagnac effect, to measure continuously, with sensitivity better than picorad/s, large bandwidth (ca. 1 kHz), and high dynamic range, the absolute angular rotation rate of the Earth. In the paper, we address the feasibility of the apparatus with respect to the ambitious specifications above, as well as prove how such an apparatus, which will be able to detect strong Earthquakes, very weak geodetic signals, as well as general relativity effects like Lense-Thirring and de Sitter, will help scientific advancements in Theoretical Physics, Geophysics, and Geodesy, among other scientific fields.

This article presents the first publicly available version of the NExt STrOng Related Earthquake (NESTORE) software (NESTOREv1.0) designed for the statistical analysis of earthquake clusters. NESTOREv1.0 is a MATLAB (www.mathworks.com/products/ matlab , last accessed August 2022) package capable of forecasting strong aftershocks starting from the first hours after the mainshocks. It is based on the NESTORE algorithm, which has already been successfully applied retrospectively to Italian and California seismicity. The code evaluates a set of features and uses a supervised machine learning approach to provide probability estimates for a subsequent large earthquake during a seismic sequence. By analyzing an earthquake catalog, the software identifies clusters and trains the algorithm on them. It then uses the training results to obtain forecasting for a test set of independent data to estimate training performance. After appropriate testing, the software can be used as an Operational Earthquake Forecasting (OEF) method for the next stronger earthquake. For ongoing clusters, it provides near-real-time forecasting of a strong aftershock through a traffic light classification aimed at assessing the level of concern. This article provides information about the NESTOREv1.0 algorithm and a guide to the software, detailing its structure and main functions and showing the application to recent seismic sequences in California. By making the NESTOREv1.0 software available, we hope to extend the impact of the NESTORE algorithm and further advance research on forecasting the strongest earthquakes during seismicity clusters.

In this paper, we propose an innovative machine learning approach called NESTORE, which analyses seismic clusters to forecast strong earthquakes of magnitudes similar or greater to those of the mainshock. The method analyzes the seismicity in the first hours/days after the mainshock and provides the probability of having a strong subsequent earthquake. The analysis is conducted at various stages of time to simulate the increase in knowledge over time. We address the main problem of statistics and machine learning when applied to spatiotemporal variation of seismicity: the small datasets available, on the order of tens or fewer instances, need a more accurate analysis with respect to the classical testing procedures, where hundreds or thousands of data are available. In addition, we develop a more robust NESTORE method based on a jackknife approach (rNESTORE), and we successfully apply it to California seismicity.

In recent years, there have been significant advances in the understanding of seismicity scaling laws, the study of spatiotemporal correlations, and earthquake clustering, with direct implications for time-dependent seismic hazard assessment. New models based on seismicity patterns, considering their physical and statistical significance, have shed light on the preparation process before large earthquakes and the evolution of clustered seismicity in time and space. On the other hand, the increasing amount of seismic data available at both local and global scales, together with accurate assessments of the reliability of the catalogs, offers new opportunities for model verification. This Special Issue brings together eight peer-reviewed articles. The articles represent a collection of innovative applications of earthquake forecasting, including the earthquake preparation process, seismic hazard assessment, statistical analysis of seismicity, synthetic catalogs, and cluster identification. It is therefore invaluable to seismologists, statistical seismologists, research students, government agencies, and academics. We are especially grateful to all the authors as without them this Special Issue would not have become a reality. As guest editors, we would like to thank the reviewers for their careful evaluation and valuable contributions. Special thanks go to Assistant Editors Carlos Sanchez and Jill Fang for their dedication to this project and their invaluable collaboration in setting up, promoting, and managing the Special Issue

In the years between 2011 and 2014, at the edge between the Apennines collapsing chain and the subducting Calabrian arc, intense seismic swarms occurred in the Pollino mountain belt. In this key region, <2.5mm/yr of NE-trending extension is accommodated on an intricate network of normal faults, having almost the same direction as the mountain belt. The long-lasting seismic release consisted of different swarm episodes, where the strongest event coinciding with a ML 5.0 shock occurred in October 2012. This latter comes after a ML four nucleated in May 2012 and followed by aseismic slip episodes. In this study, we present accurate relocations for ∼6,000 earthquakes and shear-wave splitting analysis for ∼22,600 event-station pairs. The seismicity distribution delineates two main clusters around the major shocks: in the north-western area, where the ML 5.0 occurred, the hypocenters are localized in a ball-shaped volume of seismicity without defining any planar distribution, whilst in the eastern area, where the ML 4.3 nucleates, the hypocenters define several faults of a complex system of thrusts and back-thrusts. This different behavior is also imaged by the anisotropic parameters results: a strong variability of fast directions is observed in the western sector, while stable orientations are visible in the eastern cluster. This tectonic system possibly formed as a positive flower structure but as of today, it accommodates stress on normal faults. The deep structure imaged by refined locations is overall consistent with the complex fault system recently mapped at the surface and with patterns of crustal anisotropy depicting fractures alignment at depth. The possible reactivation of inherited structures supports the important role of the Pollino fault as a composite wrench fault system along which, in the lower Pleistocene, the southward retreat of the ionian slab was accommodated; in this contest, the inversion of the faults kinematics indicates a probable southward shift of the slab edge. This interpretation may help to comprehend the physical mechanisms behind the seismic swarms of the region and defining the seismic hazard of the Pollino range: nowadays a region of high seismic hazard although no strong earthquakes are present in the historical record.

In this study, we propose an analysis of the earthquake clusters that occurred in North-Eastern Italy and western Slovenia from 1977 to today. Given a mainshock generating alarm in the population, we are interested in forecasting if a similar magnitude earthquake will follow. We classify the earthquake clusters associated with mainshocks of magnitude Mm into two classes: if the strongest aftershock has a magnitude >=Mm-1 (swarms or large aftershock seismic sequences) as type A, otherwise (smaller aftershocks seismic sequences) as type B. A large aftershock following a main shock can cause significant damages to already weakened buildings and infrastructures, so a timely advisory information to the civil protection is of great interest for effective decision-making. For the first time, we applied to a new catalogue a pattern recognition algorithm for cluster type forecasting that we developed for all Italy (Gentili and Di Giovambattista, 2017). Thanks to the lower completeness magnitude of the local OGS catalogue, compared to the national one, and to a new version of the algorithm, we were able to lower the threshold of the clusters mainshocks magnitude from 4.5 to 3.7. The method has been validated by rigorous statistical tests. We tested the algorithm on the 1976 highly destructive earthquake cluster (mainshock magnitude 6.5 - the strongest in the last 80 years in the region) and we retrospectively forecasted it as an A cluster. Successful results were obtained also on other three smaller earthquake clusters in 2019.

In this study, we analyse Lithosphere Atmosphere Ionosphere Coupling (LAIC) effects to identify some phenomena that could, possibly, be linked to the preparation phase of the MW=7.5 earthquake occurred in Indonesia on September 28th, 2018, by investigating the eight months preceding the seismic event. First, we find a seismic acceleration that started two months before the mainshock. Then, studying some physical properties of the atmosphere (skin temperature, total column water vapor and aerosol optical thickness), we find two increases of atmospheric anomalies about 6 and 3.7 months before the mainshock, and the latter one is very promising as a candidate for seismic-related phenomena. Furthermore, we investigate ionospheric disturbances, by analysing the Swarm and, for the first time, China Seismo-Electromagnetic Satellite (CSES), magnetic and electron density data during quiet geomagnetic time. From different techniques, we find interesting anomalies concentrated around 2.7 months before the mainshock. On August 19th, 2018, Swarm and CSES showed an enhancement of the electron density during night time. We critically discuss the possibility that such phenomenon can be a possible pre-seismic-induced ionospheric effect. Finally, we performed a cumulative analysis using all detected anomalies, as a test case for a possible chain of physical phenomena that could happen before the earthquake occurrence. With this study, we support the usefulness to collect and store large Earth ground and satellite observational dataset that in the future could be useful to monitor in real time the seismic zones to anticipate earthquakes, although nowadays, there is no evidence about useful prediction capabilities.

eriods of accelerated seismicity have been observed during the preparation process of many large earthquakes. This accelerating seismicity can be detected by the Accelerated Moment Release (AMR) method and its recent Revised version (RAMR) when the two techniques are applied to earthquake catalogues. The main aim of this study is to investigate the seismicity preceding large mainshocks and possibly increase our comprehension of the underlying physics. In particular, we applied both the AMR and R-AMR to the seismicity preceding 14 large worldwide shallow earthquakes, i.e. with focal depth less than 40 km, with magnitude M[6 for Mediterranean area, and M C 6.4 in the rest of the world, occurred from 2014 to 2018. Twelve case studies were analysed in the framework of SwArm For Earthquake study project funded by ESA, comprising the period 2014–2016; two additional cases were also considered to confirm the goodness of the methodology outside the period of the project catalogues. In total, R-AMR shows better performances than AMR, in 11 cases out of 14. In particular, in four out of 14 cases (i.e. 28.6%), the R-AMR method shows that acceleration exists due to an evident clustering in time–space on the faults, thus guiding the convergence of the fit; in seven cases (i.e. 50%) the R-AMR discloses acceleration, although no clustering around the fault is present; the remaining three cases (i.e. 21.4%) show no emerging acceleration from background. Finally, when R-AMR is compared with simulations, we verify that in most of the cases the acceleration is real and not casual.