Taroni, Matteo

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A seismic hazard can be quantified by using probabilities. Modern seismic forecasting models (e.g., Operational Earthquake Forecasting systems) allow us to quantify the short-term variations in such probabilities. Indeed these probabilities change with time and space, in particular after strong seismic events. However, the short-term seismic hazard could also change during seismic swarms, i.e., a sequence with several small-/medium-sized events. The goal of this work is to quantify these changes, using the Italian Operational Earthquake Forecasting system, and also estimate the variations in the Gutenberg–Richter b-value. We focus our attention on three seismic swarms that occurred in Central Italy in October–November 2023. Our results indicate that short-term variations in seismic hazard are limited, less than an order of magnitude, and also that b-value variations are not significant. Placing our findings in a more general context, we can state that according to currently available models and catalogs, the occurrence of seismic swarms does not significantly affect the short-term seismic hazard.

The new generation seismic hazard maps use 3D seismotectonic fault models, which are more consistent with the actual nature of faults, whereas the classical models based on earthquake catalogs only utilize a 2D representation of the seismicity. Although the former provides more reliable information on seismogenic structures, the latter can deliver trustworthy seismicity rates easily. Therefore, it is necessary to combine both the approaches to create a high‐quality seismic hazard assessment model. This study proposes an innovative approach using smoothed seismicity methods that can be advantageous in all contexts with available 3D fault models and high‐quality seismic catalogs. We applied our method on the Adriatic Basal Thrust (ABT) in eastern central Italy—a lithospheric‐scale active contractional structure with a well‐constrained 3D geometric–kinematic reconstruction and a related high‐quality catalog. Our new 3D algorithm was applied to smooth the ABT seismicity on the grid, resulting in a 3D earthquake rate model that also provides rupture parameters such as strike, dip, rake, and seismogenic thickness. Our approach is particularly useful for complex seismotectonic settings, such as in cases of lithospheric shear zones, subduction planes, and overlapping multidepth seismogenic volumes.

We investigate peculiar characteristics of ground motions in Southern Italy (e.g. apparent fast anelastic attenuation and trends of event terms at different periods) using a comprehensive dataset of earthquake recordings between 1969 and 2020. By doing so, we gained insights into the relative performance of eight selected region-specific, global, and global with regional adjustment ground motion models (GMMs). Our analysis is performed using a preliminary dataset (i.e. including all ground motions recorded in the area for the selected analysis period) and an independent dataset (i.e. comprising data not used to develop the models). We analyze total residuals, event terms, within-even residuals, and residuals standardized by model standard deviations (i.e. epsilon). The latter is performed to obtain a robust comparison of GMMs with different standard deviation types and levels. These approaches are employed to ground motion characterization studies for the first time in this region. Our results show that in Southern Italy, there is an apparent anelastic attenuation of the ground motion faster than in other seismic districts. Overall, regional models capture this feature better than global models. Regional adjustments to global models better capture the observed anelastic attenuation at large distances. Using the standardized residuals analysis, we observe that all selected GMMs systematically underestimate the observed ground motion for relatively high ground motion levels and its variability at any intensity levels in the study region. These outcomes may help improving future ground motion models and related engineering applications involving such models in performance-based frameworks.

earthquake forecasting and testing, ETAS model, operational earthquake forecasting, statistical seismology, short-term seismic hazard assessmen

Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametri zations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those. The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations.

The earthquake size distribution is described by an exponential function governed by the b-value parameter. It has already been proven that the b-value depends on the differential stress and tectonic settings. Here, we propose a new method to group earthquakes using the kinematics of the interseismic geodetic strain rates and horizontal stress directions. We select the Italian peninsula as a case study, and we find that the b-value is significantly larger in the extensional setting than in the compressional one, although these differences are much smaller than previously reported. We also show that spatial fragmentation of uniform tectonic regimes leads to inaccurate b-value estimation due to the undersampling of earthquake size distribution. Given these results, we conclude that stress directions and geodetic data complement other geological or geophysical information and reduce the arbitrariness in drawing zones for a seismotectonic model.

The Båth’s law is an empirical seismological relation between the magnitudes of the mainshock and the largest aftershock of a seismic sequence. This empirical law, differently from other seismological laws, could be valid only when the seismic sequence is ended. Indeed, during the sequence, we don’t know if the strongest event has already happened or not, and then inferring something about the magnitude of the largest aftershock is hazardous. In this opinion paper, we discuss some issues related to the Båth’s law: its validity on a global catalog, the use of the terms “mainshock” and “aftershock” in the seismological community and in the public, and their implications in earthquake forecasting communications. We show the uselessness of Båth’s law in earthquake forecasting, and that the words “mainshock” and “aftershock” have different interpretations for the public and for seismologists. We argue that their use during an ongoing seismic sequence, without a proper explanation of their meaning, could be confounding, in particular for the Italian language. Calling all events just “earthquakes” could be a simple but effective solution.

We estimate the b-value parameter of the Gutenberg–Richter law for earthquake magnitudes in the early stage of the Costa Marchigiana (Italy) seismic sequence, starting on 2022 November 9, with an ML 5.7 event in the Adriatic sea. In particular, we estimate both the completeness magnitude Mc and the b-value within the first 4 and 7 d after the initial strong event in the sequence. Our work represents a practical example of b-value estimation in ‘true’ real time, that is, during the seismic sequence, and its possible interpretation in terms of short-term forecasting. We highlight some critical issues to consider both in estimating/intepreting the b-value, and in evaluating the real time estimation of Mc. These issues are mainly due to the fact that preliminary catalogues available in real time are quite different from the revised ones, which are usually delivered after a few months. The criticalities are linked to the raw data recorded at an early-stage, an unreliable evaluation of the Mc with statistical approaches, the Short Term Aftershock Incompleteness entailed after the initial strong event, and the magnitude binning. Our results show that real time estimation of the b-value can give insights into the evolution of an ongoing seismic sequence, when attention is paid to data quality and quantity.

One important aspect of the seismicity is the spatiotemporal clustering; hence, the distinction between independent and triggered events is a critical part of the analysis of seismic catalogs. Stochastic declustering of seismicity allows a probabilistic distinction between these two kinds of events. Such an approach, usually performed with the epidemic‐type aftershock sequence (ETAS) model, avoids the bias in the estimation of the frequency–magnitude distribution parameters if we consider a subset of the catalog, that is, only the independent or the triggered events. In this article, we present a framework to properly include the probabilities of any event to be independent (or triggered) both in the temporal variation of the seismic rates and in the estimation of the b‐value of the Gutenberg–Richter law. This framework is then applied to a high‐definition seismic catalog in the central part of Italy covering the period from April 2010 to December 2015. The results of our analysis show that the seismic activity from the beginning of the catalog to March 2013 is characterized by a low degree of clustering and a relatively high b‐value, whereas the following period exhibits a higher degree of clustering and a smaller b‐value.