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A modern digital seismic network, with many stations optimally distributed on the earthquake causative seismic zone, enables detection of very low magnitude earthquakes and determination of their source parameters. It is essential to associate to such kind of networks procedures to analyze the huge amount of continuously recorded data for monitoring the space-time-magnitude evolution of natural and/or induced seismicity. Hence, the demand for near-real-time, automated data collection and analysis procedures for assisting seismic network operators in carrying out microearthquake monitoring is growing. In response to this need, we designed a computational software platform, TREMOR, for fast and reliable detection and characterization of seismicity recorded by a dense local seismic network. TREMOR integrates different open-source seismological algorithms for earthquake signal detection, location, and source characterizations in a fully automatic workflow. We applied the platform in play-back mode to the continuous waveform data recorded during 1 month at the Japanese Hi-net seismic network in the Nagano region (Japan) and compared the resulting catalog with the Japan Meteorological Agency bulletin in terms of number of detections, location pattern and magnitudes. The results show that the completeness magnitude of the new seismic catalog decreased by 0.35 units of the local magnitude scale and consequently the number of events increased by about 60% with respect to the available catalog. Moreover, the fault plane solutions resulted coherent with the stress regime of the region, and the Vp/Vs ratio well delineated the main structural features of the area. According to our results, TREMOR has shown to be a valid tool for investigating and studying earthquakes, especially to identify and monitor natural or induced micro-seismicity.

Nowadays, Tsunami Early Warning Systems (TEWS) issue the first alert between three and five minutes after the occurrence of a potentially tsunamigenic earthquake. For offshore earthquakes, for which a significant network azimuthal gap limits the accuracy of standard techniques for source location, this time is spent to obtain stable estimations of the event magnitude and depth. This large warning time affects the efficiency of TEWS for near-coastal large earthquakes. Recent developments in Earthquake Early Warning Systems (EEWS) mitigates this issue. Here, we consider a P-wave, shaking-forecast based EEW method (Zollo et al., 2023) to provide fast and accurate estimations of event location and magnitude along with the Potential Damage Zone (PDZ). As a first test of a combined E- and T- EWS, we applied the method by playing-back the simulated records of two events of Mw 6 and 7 in the Messina strait, with source characteristics that mimic the 1908 Messina earthquake. The events are simulated at the INGV and RAN seismic stations along the Sicily and Calabria coasts. We estimated stable and accurate hypocenter locations and magnitude determinations in 20-25 seconds for both events. The shape of the PDZ obtained after 30 s from the earthquake origin well reproduces the geometry of the rupture surface. These first results show that combining EEWS and TEWS can speed up the tsunami forecasting, thus increasing the lead-time available for actions to protect the exposed population., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

In the context of Earthquake Early Warning (EEW) applications, the high-speed rail networks are of particular interest due to their strategic role for private/public transportation. In Italy, in the frame of an industrial partnership, the University of Naples Federico II was recently commissioned by Rete Ferroviaria Italiana (RFI) S.p.A. to design, develop, and implement a prototype EEW system on the High-Speed Railway line between Rome and Naples. In case of a relevant earthquake, the system has the main goal of issuing an automatic alert that makes it possible to slow down high-speed trains, by resorting to a specific interface with existing signalling systems. The EEW methodology implements an evolutionary, P-wave based approach combining the single station and the network-based approaches. A probabilistic decision scheme for the alert declaration is implemented, accounting for the exceedance probability of a given PGA threshold value and for the uncertainty associated to the empirical scaling relationships. A final decision module evaluates whether and where the predicted PGA exceeds the established threshold and declares the alert at the nodes of the line where the most relevant shaking is expected. Here we will overview the basic principles and methodologies of the EEW platform and introduce a quantitative performance assessment, based on a retrospective off-line analysis of the system outputs. The performance analysis allows evaluating both the system rapidity in providing alerts and the quality/reliability of these predictions and represents the key element for stakeholders and end-users, to properly configure and setup the operational system., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Earthquake early warning studies are shifting real-time seismology in earthquake science. They provide methods to rapidly assess earthquakes to predict damaging ground shaking. Preventing false alarms from these systems is key. Here we developed a simple, robust algorithm, Authorizing GRound shaking for Earthquake Early warning Systems (AGREEs), to reduce falsely issued alarms. This is a network threshold-based algorithm, which differs from existing approaches based on apparent velocity of P and S waves. AGREEs is designed to function as an external module to support existing earthquake early warning systems (EEWSs) and filters out the false events, by evaluating actual shaking near the epicenter. Our retrospective analyses of the 2009 L'Aquila and 2012 Emilia earthquakes show that AGREEs could help an EEWS by confirming the epicentral intensity. Furthermore, AGREEs is able to effectively identify three false events due to a storm, a teleseismic earthquake, and broken sensors in Irpinia Seismic Network, Italy.

Over the last few decades, metropolitan areas have experienced a dramatic increase of exposure to earthquakes. Because of the still very low probability level at which short‐term earthquake forecasting is feasible, earthquake early warning systems (EEWS) currently represent an effective, pragmatic, and viable means for the implementation of protective measures to reduce the exposure of population to seismic risk. EEWS can be simply defined as systems that integrate seismic networks and software capable of performing real‐time data telemetry and analysis to provide alert messages to users within seconds of the beginning of an earthquake and certainly before that the S waves generated by the event reach the users. Worldwide, a number of EEWS capable of rapidly performing seismological analysis of ground motion during a strong earthquake are currently operative or are under real‐time testing (e.g., in Japan, Taiwan, Mexico, Italy, Turkey, California, etc.; Allen and Kanamori, 2003; Kanamori, 2005; Allen et al. , 2009). A tangible example of EEWS benefit is given by the experience of the OKI factory in the Miyagi prefecture in Japan that, after having experienced severe damages in 2003 due to two earthquakes, installed an EEWS, and at the following earthquakes experienced a significant reduction of losses (Allen et al. , 2009). Moreover, the Japan Meteorological Agency has shown the effectiveness of a combined on‐site and network‐based approach to rapidly broadcast the rapid warning after a potential damaging earthquake (Hoshiba, 2013). The key parameter of EEWS is the lead time, which is the time available for taking protective measures at distant targets once an earthquake has been promptly detected and …

The south of the Iberian Peninsula is a region in which large, damaging earthquakes occur separated by long time intervals. An example was the great 1755 Lisbon earthquake (intensity Imax¿=¿X) which occurred SW of San Vicente Cape (SW Iberian Peninsula). Due to this risk of damaging earthquakes, the implementation of Earthquake Early Warning System (EEWS) technologies is of considerable interest. With the aim of investigating the feasibility of an EEWS in this region of the Iberian Peninsula, empirical scaling relationships have been derived between the early warning parameters and the earthquake size and/or its potential damaging effects for this region. An appropriate and suitable strategy is proposed for an EEWS in the SW Iberian Peninsula, which takes into account the limitations of the existing seismological networks.

Earthquake early warning systems are able to issue an alert just after the start of the seismic rupture on the fault plane, but before the ground shaking reaches the sites to be protected. The available lead-time for security actions is very short (seconds to several dozens of seconds), requiring fast and robust methods to make these systems operational. Here, general principles about the two paradigms of early warning systems are presented: the regional and the on-site systems. When these methods are applied to data recorded by very dense networks, such as in near-fault observatories, the blind zone can be shortened to 15–25km. Next generation of early warning systems should reduce the epistemic variability by implementing modeling of finite-source and site effects. Finally, the combination of on-site and regional systems may lead to a real-time estimation of the ground-shaking maps, overcoming the problem of computing the event location, and magnitude.

The reliable determination of earthquake source parameters is a relevant task of seismological investigations which ground nowadays on high-quality seismic waveforms collected by near-source dense arrays of ground motion sensors. Here, we propose a parametric modeling technique which analyzes the time-domain P-wave signal recorded in the near-source range of small-to-large-size earthquakes. Assuming a triangular moment rate function, a uniform speed, and circular rupture model, we develop the equations to estimate the seismic moment, rupture radius, and stress drop from the corner time and plateau level of the average logarithm of the P-wave displacement versus time curves (LPDT). The constant- Q , anelastic attenuation effect, is accounted by a postprocessing procedure that evaluates the Q -unperturbed moment rate triangular shape. The methodology has been validated through application to the acceleration records of the 2016-2017 Central Italy and 2007-2019 Japan earthquake sequences covering a wide moment magnitude range (Mw 2.5-6.5) and recording distance 5.5 events in Central Italy relative to previous estimates using spectral methods.

Water injection in geothermal areas is the preferential strategy to sustain the natural production of geothermal resources. In this context, monitoring microearthquakes is a fundamental tool to track changes in the reservoirs in terms of soil composition, response to injections, and resource exploitation with space and time. Therefore, refined source characterization is crucial to better estimate the size, source mechanism, and rupture process of microearthquakes, as they are possibly related to industrial activities, and to identify any potential variation in the background seismicity. Standard approaches for source parameter estimation are ordinarily based on the modelling of Fourier displacement spectra and its characteristic parameters: the low-frequency spectral level and corner frequency. Here, we apply an innovative time domain technique that uses the curves of P-wave amplitude vs. time along the seismogram. This methodology allows estimation of seismic moment, source radius, and stress release from the plateau level and the corner time of the average logarithm of P-wave displacement versus time with the assumption of a triangular moment rate function, uniform rupture speed, and a constant/frequency-independent Q-factor. In the current paper, this time domain methodology is implemented on a selected catalog of microearthquakes consisting of 83 events with a moment magnitude ranging between 1.0 and 1.5 that occurred during a 7-year period (2007–2014) of fluid extraction/injection around Prati-9 and Prati-29 wells at The Geysers geothermal field. The results show that the time domain technique provides accurate seismic moment (moment magnitude) and rupture duration/radius estimates of microearthquakes down to the explored limit (M 1) while accounting for the anelastic attenuation effect in the radiated high-frequency wavefield. The retrieved source radius vs. moment scaling is consistent with a self-similar, constant stress drop scaling model, which proves an appropriate attenuation correction and the validity of the assumed, triangular moment rate function for microearthquake ruptures. Two alternative mechanical models are proposed to explain the observed difference (about two orders of magnitude) in the retrieved average stress release estimates between the time and frequency domain methods. We argue that the two quantities may not refer to the same physical quantity representing the stress release of earthquake ruptures. Either the smaller stress release values from the time domain method may indicate a larger fracture area (by a factor of 20) radiating the observed P-waveforms than the one estimated from the corner frequencies, or the frequency domain estimate is a proxy for dynamic stress release while the time domain is more representative of the static release. The latter is associated with a much lower dynamic friction value than static friction value at the fault during the rupture process.

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M > 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019.

A primary task of a network-based, earthquake early warning system is the prompt event detection and location, needed to assess the magnitude of the event and its potential damage through the predicted peak ground shaking amplitude using empirical attenuation relationships. Most of real-time, automatic earthquake location methods ground on the progressive measurement of the first P-wave arrival time at stations located at increasing distances from the source but recent approaches showed the feasibility to improve the accuracy and rapidity of the earthquake location by using the additional information carried by the P-wave polarization or amplitude, especially with unfavorable seismic network lay-outs. Here, we propose an evolutionary, Bayesian method for the real-time earthquake location which combines the information derived from the differential P-wave arrival times, amplitude ratios and back-azimuths measured at a minimum of two stations. As more distant stations record the P-wave, the posterior pdf is updated and new earthquake location parameters are determined along with their uncertainty. To validate the location method, we performed a retrospective analysis of mainshocks (M > 4.5) occurred during the 2016–2017 Central Italy earthquake sequence by simulating the typical acquisition layouts of in-land, coastal and linear array of stations. Results show that with the combined use of the three parameters, 2–4 s after the first P-wave detection, the method converges to stable and accurate determinations of epicentral coordinates and depth even with a nonoptimal coverage of stations. The proposed methodology can be generalized and adapted to the off-line analysis of seismic records collected by standard local networks.

In this work we propose and apply a straightforward methodology for the automatic characterization of the extended earthquake source, based on the progressive measurement of the P-wave displacement amplitude at the available stations deployed around the source. Specifically, we averaged the P-wave peak displacement measurements among all the available stations and corrected the observed amplitude for distance attenuation effect to build the logarithm of amplitude vs. time function, named LPDT curve. The curves have an exponential growth shape, with an initial increase and a final plateau level. By analyzing and modelling the LPDT curves, the information about earthquake rupture process and earthquake magnitude can be obtained. We applied this method to the Chinese strong motion data from 2007 to 2015 with Ms ranging between 4 and 8. We used a refined model to reproduce the shape of the curves and different source models based on magnitude to infer the source-related parameters for the study dataset. Our study shows that the plateau level of LPDT curves has a clear scaling with magnitude, with no saturation effect for large events. By assuming a rupture velocity of 0.9 Vs, we found a consistent self-similar, constant stress drop scaling law for earthquakes in China with stress drop mainly distributed at a lower level (0.2 MPa) and a higher level (3.7 MPa). The derived relation between the magnitude and rupture length may be feasible for real-time applications of Earthquake Early Warning systems.

The study of seismic source parameters is crucial for understanding the origin of seismicity and retrieving information on the energy balance and the stress involved in earthquake rupture processes. In active tectonic areas, where underground industrial activities are carried out, such parameters may help to understand whether earthquakes are induced, triggered, or natural. The Montello-Collalto area (Southeastern Alps) is located in an active tectonic environment and hosts a depleted natural reservoir used to store gas. Since 2012, a high-quality seismic network monitors the microseismicity occurring around the underground gas storage reservoir to understand if the storage activity might induce seismicity. In this paper, we estimate the source parameters of low magnitude events representative of the seismicity occurring in the area surrounding the reservoir. The analysis includes a preliminary removal of the site effects, specifically computed within this study, from all the records. Then, using a parametric multistep inversion scheme, we estimate the seismic moment, the corner frequency and the static stress drop, that can be set as reference for the microseismicity occurring in the study area. All the investigated earthquakes show low seismic efficiency compatible with overshoot processes, which is typical of natural (i.e., tectonic) earthquakes. Our procedure can be implemented in other tectonic regions hosting underground industrial activities to support the decisional processes related to real-time monitoring.

When an earthquake occurs, a key parameter in the emergency’s management is the knowledge of the most stressed areas by the ground motion. The focal mechanism is an essential source parameter for producing realistic shake maps. Although the approaches for estimating earthquake location and magnitude are now consolidated, automatic solutions for the focal mechanism are not always provided by the agencies or available at later times after inversion of waveforms for the determination of moment tensor components. We introduce a new approach for the automatic determination of the earthquake focal mechanism, using the spatial distribution of observed absolute initial P-wave peak amplitudes, corrected for the geometrical attenuation effect, in an evolutionary, Bayesian framework. We applied the proposed methodology to the main earthquakes of the 2016–2017 central Italy seismic sequence finding that our rapid automatic estimates of the focal mechanism are in good agreement with those of the reference solutions.

Despite their importance for eruption forecasting the causes of seismic rupture processes during caldera unrest are still poorly reconstructed from seismic images. Seismic source locations and waveform attenuation analyses of earthquakes in the Campi Flegrei area (Southern Italy) during the 1983–1984 unrest have revealed a 4–4.5 km deep NW-SE striking aseismic zone of high attenuation offshore Pozzuoli. The lateral features and the principal axis of the attenuation anomaly correspond to the main source of ground uplift during the unrest. Seismic swarms correlate in space and time with fluid injections from a deep hot source, inferred to represent geochemical and temperature variations at Solfatara. These swarms struck a high-attenuation 3–4 km deep reservoir of supercritical fluids under Pozzuoli and migrated towards a shallower aseismic deformation source under Solfatara. The reservoir became aseismic for two months just after the main seismic swarm (April 1, 1984) due to a SE-to-NW directed input from the high-attenuation domain, possibly a dyke emplacement. The unrest ended after fluids migrated from Pozzuoli to the location of the last caldera eruption (Mt. Nuovo, 1538 AD). The results show that the high attenuation domain controls the largest monitored seismic, deformation, and geochemical unrest at the caldera.

Online Material: Figures showing local magnitude versus duration; table of station correction coefficients. The earthquake magnitude estimate is a routine task in all seismological observatories. Several magnitude scales are available, based on amplitude measurement of different seismic phases, and/or on total signal duration. Among them, the duration magnitude ( M D) is adopted in many regional networks because it provides a rapid and reliable estimate of the earthquake size through a fairly simple procedure based on the measure of the duration of recorded seismograms. Bisztricany (1958) first demonstrated the existence of a relationship between magnitude and duration, and several authors (e.g., Sole’vev, 1965; Tsumura, 1967; Bakun, 1984; Vidal and Munguia, 2005; Hara, 2007; among many others) later discussed the use of duration of the recorded seismograms as a measure of the event size. Furthermore, in a recent paper, Lomax and Michelini (2009) proposed a duration magnitude procedure for the rapid determination of the moment magnitude, based on the P ‐wave recordings at teleseismic distances, which can be applied for tsunami early warning. In its more general formulation the duration magnitude depends on the ground‐shaking duration, on the hypocentral distance, and accounts for a station correction coefficient. According to Real and Teng (1973) and Hermann (1975), the duration magnitude is defined as ![Graphic][1] (1)in which τ is the signal duration, R is the hypocentral distance, Sc stands for the station correction, and a , b , and c are coefficients to be determined through a regression analysis. The present work has a double goal. First, we present an automatic procedure, which has been specifically developed to measure the earthquake duration, based on the estimate of the signal‐to‐noise ratio (SNR) along the seismic records. We then calibrate a duration magnitude scale for the area monitored by the … [1]: /embed/inline-graphic-1.gif

We present an approach to Earthquake Early Warning for Southern Italy that integrates regional and on-site systems. The regional approach is based on the PRobabilistic and Evolutionary early warning SysTem (PRESTo) software platform. PRESTo processes 3-components acceleration data streams and provides a peak ground-motion prediction at target sites based on earthquake location and magnitude computed from P-wave analysis at few stations in the source vicinity. On the other hand, the on-site system is based on the real-time measurement of peak displacement and dominant period, on a 3 s P-wave time-window. These values are compared to thresholds, set for a minimum magnitude 6 and instrumental intensity VII, derived from empirical regression analyses on strong-motion data. Here we present an overview of the system and describe the algorithms implemented in the PRESTo platform.We also show some case-studies and propose a robust methodology to evaluate the performance of this Early Warning System.

Earthquake Early Warning Systems (EEWS) are real-time, seismic monitoring infrastructures that are able to provide a rapid notification of the potential damaging effects of an impending earthquake. This objective is achieved through the fast telemetry and processing of data from dense instrument arrays deployed in the source region of the event of concern (regional EEWS) or surrounding/at the target infrastructure (front-detection or site-specific EEWS). A regional EEWS is based on a dense sensor network covering a portion or the entire area that is threatened by earthquakes. The relevant source parameters (event location and magnitude) are estimated from the early portion of recorded signals (initial P-waves) and are used to predict, with a quantified confidence, a ground-motion intensity measure at a distant site where a target structure of interest is located. Site-specific (or on-site) EEWS consist of a single sensor or an array of sensors deployed in the proximity of the target structure that is to be alerted and whose measurements of amplitude and predominant period on the initial P-wave motion are used to predict the ensuing peak ground motion (mainly related to the arrival of Sand surface waves) at the same site. Frontdetection EEWS is essentially a variant of the on-site approach, where a barrier-shaped, accelerometric network is deployed between the source region and the target site to be protected. The alert is issued when two or more nodes of the array record a ground acceleration amplitude larger than a default threshold value. For typical regional distances, the peak acceleration at the barrier nodes is expected to be associated with the S-wave train, so that the distance between the network and the target is set to maximize the lead time (i.e., the time available for warning before the arrival of strong ground shaking at the target sites), which is, in this case, the travel time of S-waves from the barrier to the target site. EEWS have experienced a very rapid improvement and a wide diffusion in many active seismic regions of the world in the last three decades (Fig. 1). They are operating in Japan, Taiwan, Mexico, and California. Many other systems are under development and testing in other regions of the world such as in Italy, Turkey, Romania, and China. Most of these existing EEWS essentially operate in the two different configurations described above, i.e., regional and on-site, depending on the source-tosite distance and on the geometry of the considered network with respect to the source area. The “front-detection” EEWS such as the barrier-type, Seismic Alert System (Espinosa-Aranda

Most of existing earthquake early-warning systems are regional or on-site systems. A new concept is the integration of these approaches for the definition of alert levels and the estimation of the earthquake potential damage zone (PDZ). The key element of the method is the real-time, simultaneous measurement of initial peak displacement (P-d) and period parameter (tau(c)) in a 3-s window after the first P-wave arrival time at accelerometer stations located at increasing distances from the epicenter. As for the on-site approach, the recorded values of P-d and tau(c) are compared to threshold values, which are set for a minimum magnitude M 6 and instrumental intensity I-MM VII, according to empirical regression analysis of strong-motion data from different seismic regions. At each recording site the alert level is assigned based on a decisional table with four entries defined by threshold values of the parameters P-d and tau(c). A regional network of stations provides the event location and transmits the information about the alert levels recorded at near-source stations to more distant sites, before the arrival of the most destructive phase. We present the results of performance tests of this method using ten M > 6 Japanese earthquakes that occurred in the period 2000-2009 and propose a very robust methodology for mapping the PDZ in the first seconds after a moderate-to-large earthquake. The studied cases displayed a very good matching between the rapidly predicted earthquake PDZ inferred from initial P-peak displacement amplitudes and the instrumental intensity map, the latter being mapped after the event, using peak ground velocity and/or acceleration, or from field macroseismic surveys.