ALBERT

Description: 〈span〉〈div〉SUMMARY〈/div〉Microseismic monitoring is a primary tool for understanding and tracking the progress of mechanical processes occurring in active rock fracture systems. In geothermal or hydrocarbon fields or along seismogenic fault systems, the detection and location of microseismicity facilitates resolution of the fracture system geometry and the investigation of the interaction between fluids and rocks, in response of stress field perturbations. Seismic monitoring aims to detect locate and characterize seismic sources. The detection of weak signals is often achieved at the cost of increasing the number of false detections, related to transient signals generated by a range of noise sources, or related to instrumental problems, ambient conditions or human activity that often affect seismic records. A variety of fast and automated methods has been recently proposed to detect and locate microseismicity based on the coherent detection of signal anomalies, such as increase in amplitude or coherent polarization, at dense seismic networks. While these methods proved to be very powerful to detect weak events and to reduce the magnitude of completeness, a major problem remains to discriminate among weak seismic signals produced by microseismicity and false detections. In this work, the microseimic data recorded along the Irpinia fault zone (Southern Apennines, Italy) are analysed to detect weak, natural earthquakes using one of such automated, migration-based, method. We propose a new method for the automatic discrimination of real vs false detections, which is based on empirical data and information about the detectability (i.e. detection capability) of the seismic network. Our approach allows obtaining high performances in detecting earthquakes without requiring a visual inspection of the seismic signals and minimizing analyst intervention. The proposed methodology is automated, self-updating and can be tuned at different success rates.〈/span〉

Description: Structural health monitoring (SHM) aims to improve knowledge of the safety and maintainability of civil structures. The usage of recording systems exploiting wireless communication technology is particularly suitable for SHM, especially for rapid response following earthquakes. In this study, both of these issues are combined, and we report on the application of seismic interferometry to SHM using a dataset of seven earthquakes collected using a novel wireless system of accelerometers during the L'Aquila, Italy, seismic sequence in 2009. We show that interferometric analysis allows the estimation of the shear-wave velocity of seismic phases propagating throughout a structure, and, most important for SHM purposes, allows the monitoring of the velocity variations during the aftershock sequence. Moreover, innovatively we apply the S transform to the building response functions retrieved by interferometry to estimate the fundamental resonance frequency and the quality factor Q.

Description: 〈span〉〈div〉Abstract〈/div〉We derive a set of regional ground‐motion prediction equations (GMPEs) in the Fourier amplitude spectra (FAS‐GMPE) and in the spectral acceleration (SA‐GMPE) domains for the purpose of interpreting the between‐event residuals in terms of source parameter variability. We analyze a dataset of about 65,000 recordings generated by 1400 earthquakes (moment magnitude 2.5≤Mw≤6.5, hypocentral distance Rhypo≤150  km) that occurred in central Italy between January 2008 and October 2017. In a companion article (〈a href="https://pubs.geoscienceworld.org/bssa#rf12"〉Bindi, Spallarossa, 〈span〉et al.〈/span〉, 2018〈/a〉), the nonparametric acceleration source spectra were interpreted in terms of ω‐square models modified to account for deviations from a high‐frequency flat plateau through a parameter named ksource. Here, the GMPEs are derived considering the moment (Mw), the local (ML), and the energy (Me) magnitude scales, and the between‐event residuals are computed as random effects. We show that the between‐event residuals for the FAS‐GMPE implementing Mw are correlated with stress drop, with correlation coefficients increasing with increasing frequency up to about 10 Hz. Contrariwise, the correlation is weak for the FAS‐GMPEs implementing ML and Me, in particular between 2 and 5 Hz, where most of the corner frequencies lie. At higher frequencies, all models show a strong correlation with ksource. The correlation with the source parameters reflects in a different behavior of the standard deviation τ of the between‐event residuals with frequency. Although τ is smaller for the FAS‐GMPE using Mw below 1.5 Hz, at higher frequencies, the model implementing either ML or Me shows smaller values, with a reduction of about 30% at 3 Hz (i.e., from 0.3 for Mw to 0.1 for ML). We conclude that considering magnitude scales informative for the stress‐drop variability allows to reduce the between‐event variability with a significant impact on the hazard assessment, in particular for studies in which the ergodic assumption on site is removed.〈/span〉

Description: 〈span〉〈div〉Summary〈/div〉We introduce in the on-site earthquake early warning (EEW) a partially non-ergodic perspective from the site effects point of view. We consider the on-site EEW approach where the peak ground velocity (PGV) for S-waves is predicted from an early estimate, over the P-waves, of either the peak-displacement (PD) or cumulative squared velocity (IV2). The empirical PD-PGV and IV2-PGV relationships are developed by applying a mixed-effect regression where the site-specific modifications of ground shaking are treated as random effects. We considered a large data set composed of almost 31000 selected recordings in central Italy, a region struck by four earthquakes with magnitude between 6 and 6.5 since the 2009 L’Aquila earthquake. We split the data set into three subsets used for calibrating and validating the on-site EEW models, and for exemplifying their application to stations installed after the calibration phase. We show that the partially non-ergodic models improve the accuracy of the PGV predictions with respect to ergodic models derived for other regions of the world. Moreover, considering PD and accounting for site effects, we reduce the (apparent) aleatory variability of the logarithm of PGV from 0.31-0.36, typical values for ergodic on-site EEW models, to about 0.25. Interestingly, a lower variability of 0.15 is obtained by considering IV2 as proxy, which suggests further consideration of this parameter for the design of on-site EEW systems. Since being site-specific is an inherent characteristic of on-site EEW applications, the improved accuracy and precision of the PGV predicted for a target protection translate in a better customization of the alert protocols for automatic actions.〈/span〉

Description: 〈span〉〈div〉Summary〈/div〉The size of an earthquake can be defined either from the seismic moment (M〈sub〉0〈/sub〉) or in terms of radiated seismic energy (E〈sub〉r〈/sub〉). These two parameters look at the source complexity from different perspectives: M〈sub〉0〈/sub〉 is a static measure of the earthquake size, whereas E〈sub〉r〈/sub〉 is related to the rupture kinematics and dynamics. For practical applications and for dissemination purposes, the logarithms of M〈sub〉0〈/sub〉 and E〈sub〉r〈/sub〉 are used to define the moment magnitude M〈sub〉w〈/sub〉 and the energy magnitude M〈sub〉E〈/sub〉, respectively. The introduction of M〈sub〉w〈/sub〉 and M〈sub〉E〈/sub〉 partially obscure the complementarity of M〈sub〉0〈/sub〉 and E〈sub〉r〈/sub〉. The reason is due to the assumptions needed to define any magnitude scale. For example, in defining M〈sub〉w〈/sub〉, the apparent stress (i.e. the ratio between M〈sub〉0〈/sub〉 and E〈sub〉r〈/sub〉 multiplied by the rigidity) was assumed to be constant, and under this condition, M〈sub〉w〈/sub〉 and M〈sub〉E〈/sub〉 values would only differ by an off-set which, in turn, depends on the average apparent stress of the analysed dataset. In any case, when the apparent stress is variable and, for example, scales with M〈sub〉0〈/sub〉, the value of M〈sub〉E〈/sub〉 derived from M〈sub〉w〈/sub〉 cannot be used to infer E〈sub〉r〈/sub〉.In this study, we investigate the similarities and differences between M〈sub〉w〈/sub〉 and M〈sub〉E〈/sub〉 in connection with the scaling of the source parameters using a dataset of around 4700 earthquakes recorded at both global and regional scales and belonging to four datasets. These cover different geographical areas and extensions and are composed by either natural or induced earthquakes in the magnitude range 1.5 ≤ M〈sub〉w〈/sub〉 ≤ 9.0. Our results show that M〈sub〉E〈/sub〉 is better than M〈sub〉w〈/sub〉 in capturing the high-frequency ground shaking variability whenever the stress drop differs from the reference value adopted to define M〈sub〉w〈/sub〉. We show that M〈sub〉E〈/sub〉 accounts for variations in the rupture processes, introducing systematic event-dependent deviations from the mean regional peak ground motion velocity scaling. Therefore, M〈sub〉E〈/sub〉 might be a valid alternative to M〈sub〉w〈/sub〉 for deriving ground motion prediction equations for seismic hazard studies in areas where strong systematic stress drop scaling with M〈sub〉0〈/sub〉 are found, such as observed for induced earthquakes in geothermal regions. Furthermore, we analyse the different datasets in terms of their cumulative frequency-magnitude (CFM) distribution, considering both M〈sub〉E〈/sub〉 and M〈sub〉w〈/sub〉. We show that the b values from M〈sub〉w〈/sub〉 (〈span〉b〈/span〉〈sub〉Mw〈/sub〉) and M〈sub〉E〈/sub〉 (〈span〉b〈/span〉〈sub〉ME〈/sub〉) can be significantly different when the stress drop shows a systematic scaling relationship with M〈sub〉0〈/sub〉. We found that 〈span〉b〈/span〉〈sub〉ME〈/sub〉 is nearly constant for all datasets, while 〈span〉b〈/span〉〈sub〉Mw〈/sub〉 shows an inverse linear scaling with apparent stress.〈/span〉

Description: 〈span〉〈div〉Abstract〈/div〉We derive a set of regional ground‐motion prediction equations (GMPEs) in the Fourier amplitude spectra (FAS‐GMPE) and in the spectral acceleration (SA‐GMPE) domains for the purpose of interpreting the between‐event residuals in terms of source parameter variability. We analyze a dataset of about 65,000 recordings generated by 1400 earthquakes (moment magnitude 2.5≤Mw≤6.5, hypocentral distance Rhypo≤150  km) that occurred in central Italy between January 2008 and October 2017. In a companion article (〈a href="https://pubs.geoscienceworld.org/bssa#rf12"〉Bindi, Spallarossa, 〈span〉et al.〈/span〉, 2018〈/a〉), the nonparametric acceleration source spectra were interpreted in terms of ω‐square models modified to account for deviations from a high‐frequency flat plateau through a parameter named ksource. Here, the GMPEs are derived considering the moment (Mw), the local (ML), and the energy (Me) magnitude scales, and the between‐event residuals are computed as random effects. We show that the between‐event residuals for the FAS‐GMPE implementing Mw are correlated with stress drop, with correlation coefficients increasing with increasing frequency up to about 10 Hz. Contrariwise, the correlation is weak for the FAS‐GMPEs implementing ML and Me, in particular between 2 and 5 Hz, where most of the corner frequencies lie. At higher frequencies, all models show a strong correlation with ksource. The correlation with the source parameters reflects in a different behavior of the standard deviation τ of the between‐event residuals with frequency. Although τ is smaller for the FAS‐GMPE using Mw below 1.5 Hz, at higher frequencies, the model implementing either ML or Me shows smaller values, with a reduction of about 30% at 3 Hz (i.e., from 0.3 for Mw to 0.1 for ML). We conclude that considering magnitude scales informative for the stress‐drop variability allows to reduce the between‐event variability with a significant impact on the hazard assessment, in particular for studies in which the ergodic assumption on site is removed.〈/span〉

Description: The accurate determination of stress drop, seismic efficiency and how source parameters scale with earthquake size is an important issue for seismic hazard assessment of induced seismicity. We propose an improved non-parametric, data-driven strategy suitable for monitoring induced seismicity, which combines the generalized inversion technique together with genetic algorithms. In the first step of the analysis the generalized inversion technique allows for an effective correction of waveforms for attenuation and site contributions. Then, the retrieved source spectra are inverted by a non-linear sensitivity-driven inversion scheme that allows accurate estimation of source parameters. We therefore investigate the earthquake source characteristics of 633 induced earthquakes (M w 2-3.8) recorded at The Geysers geothermal field (California) by a dense seismic network (i.e., 32 stations, more than 17.000 velocity records). We find a non-self-similar behavior, empirical source spectra that require an ω γ source model with γ 〉 2 to be well fit and small radiation efficiency η SW . All these findings suggest different dynamic rupture processes for smaller and larger earthquakes, and that the proportion of high frequency energy radiation and the amount of energy required to overcome the friction or for the creation of new fractures surface changes with earthquake size. Furthermore, we observe also two distinct families of events with peculiar source parameters that in one case suggests the reactivation of deep structures linked to the regional tectonics, while in the other supports the idea of an important role of steeply dipping faults in the fluid pressure diffusion.