Variability in Anelastic Attenuation: Examining Temporal and Spatial Influences on Ground Motion Characteristics in Central Italy

Abstract

In recent decades, Central Italy has faced several seismic sequences, such as the one of the 2016-2017, started with the Amatrice mainshock (Mw6.2) in August 2016, followed by the Visso (Mw5.9) and Norcia (Mw6.5) earthquakes in October 2016 (hereafter AVN). Given the region's frequent seismic activity and heigh seismic risk, the use of ground-motion simulations becomes crucial for seismic risk assessment and earthquake engineering applications. Ground motion characteristics have been already investigated in the area for the Mw6.2 Amatrice and Mw6.5 Norcia earthquakes, using stochastic and numerical approaches. Moreover, to predict ground motion from hypothesized events, it is fundamental to define the attenuation characteristics of the area and the relationship with the ground motion models. In this study, we investigate the variability of seismic wave attenuation in strong-ground motion simulation in the Central Apennines, employing stochastic simulations. Initially, we compute the quality factor Q values for the region, deriving the total attenuation Q as a function of frequency for the 2016-2017 seismic sequence. To visualize the variation of the attenuation, a 2D kernel-based imaging of coda-Q space is applied, confirming an increment in attenuation during the AVN sequence in the fault plane zones. Then, we incorporate the acquired frequency-dependent Q values as input parameters into the simulations of ground motion. This methodology replicates stochastically strong-ground motion at high frequencies, reproducing horizontal and vertical accelerograms and using as input information from the source (stress drop, rupture time, slip distribution and radiation pattern). The estimations are then correlated and validated against observed peak ground accelerations and spectral accelerations for the Amatrice and Norcia fault planes, followed by a comparison with the ground motion prediction equations used for the region.