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The region of central Italy is well known for its moderate-to-large earthquakes. Events such as 2016 Mw 6.2 Amatrice, generated in the shallow extensional tectonic regime, motivate numerical simulations to gain insights into source-related ground-motion complexities. We utilize a hybrid integral-composite kinematic rupture model by Gallovič and Brokešová (2007) to predict ground motions for other hypothetical Amatrice fault rupture scenarios (scenario events). The synthetic seismograms are computed in 1D crustal velocity models, including region-specific 1D profiles for selected stations up to 10 Hz. We create more than ten thousand rupture scenarios by varying source parameters. The resulting distributions of synthetic spectral accelerations at periods 0.2-2 s agree with the empirical nonergodic ground-motion model of Sgobba et al. (2021) for central Italy in terms of the mean and total variability. However, statistical mixed-effect analysis of the residuals indicates that the between-event variability of the scenarios exceeds the empirical one significantly. We quantify the role of source model parameters in the modeling and demonstrate the pivotal role of the so-called stress parameter that controls high-frequency radiation. We propose restricting the scenario variability to keep the between-event variability within the empirical value. The presented validation of the scenario variability can be generally utilized in scenario modeling for more realistic physics-based seismic hazard assessment. [ABSTRACT FROM AUTHOR]

We investigated the temporal variation of the spectral decay parameter [Math Processing Error] before and after two main earthquakes that occurred in the central Italy region, namely the Amatrice ([Math Processing Error] 6.0) of 24 August 2016 and the Norcia ([Math Processing Error] 6.5) of 30 October 2016 earthquakes. For this analysis, we used seismograms from the central Italy dense seismic array stations, and earthquakes located at hypocenter distances r < 80 km, having magnitudes [Math Processing Error] 3.4-6.5. The dataset consists of 393 events recorded at 92 stations. We estimated, for both earthquake sequences, average functions [Math Processing Error] that describe the distance dependence of [Math Processing Error] along the S-wave source-station paths using acceleration spectra from foreshocks, mainshock, and aftershocks. We observed that there was a regional attenuation drop within approximately two months after the Amatrice earthquake. Then, [Math Processing Error] tends to return toward the attenuation values observed before the occurrence of the main event, namely to the values of [Math Processing Error] obtained from the foreshocks, when the earthquake cycle is probably completed. We also estimated the near-source kappa ([Math Processing Error]) using aftershocks from 24 August 2016 to 3 September 2016. The results show that the values of [Math Processing Error] are lower than those from aftershocks located to the north near the epicenter of the Amatrice earthquake, suggesting that the tectonic stress was probably high near the rupture zone, and that there may be a likely fluid flow of crustal fluids. [Math Processing Error] obtained from the foreshocks of the Norcia earthquake is like that calculated with the records of the Amatrice aftershocks. Then, [Math Processing Error] drops to lower attenuation values during the Norcia main event and tends to increase again during the aftershocks. From the analysis of these two earthquake sequences that occurred in a short-time interval in central Italy, we conclude that the temporal variation of [Math Processing Error] could be a valuable indicator to monitor the earthquake cycle. [ABSTRACT FROM AUTHOR]

In this article, we implement a new approach to calibrate ground-motion models (GMMs) characterized by spatially varying coefficients, using the calibration dataset of an existing GMM for crustal events in Italy. The model is developed in the methodological framework of the multisource geographically weighted regression (MS-GWR, Caramenti et al., 2020), which extends the theory of multiple linear regression to the case with model coefficients that are spatially varying, thus allowing for capturing the multiple sources of nonstationarity in ground motion related to event and station locations. In this way, we reach the aim of regionalizing the ground motion in Italy by specializing the model in a nonergodic framework. Such an attempt at regionalization also addresses the purpose of capturing the regional effects in the modeling, which is needed for the Italian country, where ground-motion properties vary significantly across space. Because the proposed model relies on the italian GMM (ITA18) (Lanzano et al., 2019) dataset and functional form, it could be considered the ITA18 nonstationary version, thus allowing one to predict peak ground acceleration and velocity, as well as 36 ordinates of the 5%-damped acceleration response spectra in the period interval T=0.01-10 s. The resulting MS-GWR model shows an improved ability to predict the ground motion locally, compared with stationary ITA18, leading to a significant reduction of the total variability at all periods of about 15%-20%. The article also provides scenario-dependent uncertainties associated with the median predictions to be used as a part of the epistemic uncertainty in the context of probabilistic seismic hazard analyses. Results show that the approach is promising for improving the model predictions, especially on densely sampled areas, although further studies are necessary to resolve the observed trade-off inherent to site and path effects, which limits their physical interpretation. [ABSTRACT FROM AUTHOR]

We present an extended and updated version of the worldwide NEar-Source Strong-motion (NESS) flat file, which includes an increased number of moderate-to-strong earthquakes recorded in epicentral area, new source metadata and intensity measures, comprising spectral displacements and fling-step amplitudes retrieved from the extended baseline correction processing of velocity time series. The resulting dataset consists of 81 events with moment magnitude≥5.5 and hypocentral depth shallower than 40 km, corresponding to 1189 three-component waveforms, which are selected to have a maximum source-to-site distance within one fault length. Details on the flat files, metadata, and ground-motion parameters, processing scheme, and statistical findings are presented and discussed. The analysis of these data allows recognizing the presence of distinctive features (such as pulse-like waveforms, large vertical components, and hanging-wall effects) that can be exploited to assess their impact on near-source seismic motion. As an example, we use the NESS2.0 dataset for calibrating an empirical correction factor of a regional ground-motion model (GMM) mainly based on far-field records. In this way, we can adjust the median predictions to account for near-source effects not fully captured by the reference model. The final goal of this work is to promote the use of the NESS2 flat file as a tool to disseminate qualified and referenced near-source data and metadata in the light of improving the constraints of GMMs (both empirical and physics-based) close to the source. [ABSTRACT FROM AUTHOR]

In this study, we propose an approach to generate spatially correlated seismic ground-motion fields for loss assessment and risk analysis. Differently from the majority of spatial correlation models, usually calibrated on within-earthquake residuals, we use the sum of the source-, site-, and path-systematic effects (namely corrective terms) of the ground-motion model (GMM), obtained relaxing the ergodic assumption. In this way, we build a scenario-related spatial correlation model of the corrective terms by which adjusting the median predictions of ground motion and the associated variability. We show a case study focused on the Po Plain area in northern Italy, presenting a series of peculiar features (i.e., availability of a dense dataset of seismic records with uniform soil classification and very large plain with variable thickness of the sedimentary cover) that make its study particularly suitable for the purpose of developing and validating the proposed approach. The study exploits the repeatable corrective terms, estimated by Lanzano et al. (2017) in northern Italy, using a local GMM (Lanzano et al., 2016), which predicts the geometric mean of horizontal response spectral accelerations in the 0.01-4 s period range. Our results show that the implementation of a spatially correlated model of the systematic terms provides reliable shaking fields at various periods and spatial patterns compliant with the deepest geomorphology of the area, which is an aspect not accounted by the GMM model. The possibility to define a priori fields of systematic effects depending on local characteristics could be usefully adopted either to simulate future ground-motion scenarios or to reconstruct past events. [ABSTRACT FROM AUTHOR]

This work aims to revise the Bindi et al. (2011) ground-motion model for shallow crustal earthquakes in Italy (hereinafter, ITA10), calibrated in the magnitude range 4.0-6.9 using strong-motion data recorded up to the 2009 L'Aquila sequence. The improvement of ITA10 is needed because of the large number of strong-motion records made available in Italy after the occurrence of the most recent seismic sequences (2012 Emilia, Northern Italy; 2016-2017 Central Italy). The new data collection allows us to extend the magnitude range beyond 6.9 and to include vibration periods up to 10 s. Instead of the geometric mean of the horizontal components of ground motion, the median of orientation independent amplitudes (RotD50) is selected as a measure of the ground-motion parameters, and the rupture distance is introduced as an alternative source-to-site metric to the Joyner-Boore distance (RJB). The site effects are accounted for by a linear dependence on the time-averaged shear-wave velocity in the upper 30 m, VS30. A breakdown of the ground-motion variability is performed into between-event and site-to-site components to make the model suitable for the evaluation of nonergodic probabilistic seismic hazard. We also build a heteroscedastic model for aleatory variability as a function of moment magnitude and VS30. The evaluation of the epistemic uncertainty in the median prediction is also provided to be introduced in the logic trees for the probabilistic seismic hazard assessment. We obtain changes in median predictions with respect to ITA10 at distances lower than 10 km and for strong events (Mw>6.5); moreover, the total standard deviations are significantly lower at intermediate and long periods, with an average reduction of about 20%. [ABSTRACT FROM AUTHOR]

This work proposes a novel approach to the calibration of regionalized regression models, with particular reference to ground-motion models (GMMs), which are key for probabilistic seismic hazard analysis and earthquake engineering applications. A novel methodology, named multi-source geographically-weighted regression (MS-GWR), is developed, allowing one to (i) estimate regionalized regression models depending on multiple sources of non-stationarity (such as site- and event-dependent non-stationarities in GMMs), and (ii) make inference on the significance and stationarity of the regression coefficients. Unlike previous approaches to the problem, the proposed framework is non-parametric – in the sense of the distribution of the errors – the inference being based on a permutation scheme. MS-GWR is here used to calibrate a new regionalized ground-motion model for predicting peak ground acceleration in Italy, based on a large scale database of waveforms and metadata made available by the Italian Institute for Geophysics and Vulcanology (INGV). [ABSTRACT FROM AUTHOR]