ABSTRACT We develop a new nonergodic and nonstationary (NENS) ground-motion prediction model for central Italy, leveraging an extensive strong-motion dataset including the 2016–2017 Amatrice–Visso–Norcia sequence. The model is formulated as a nested mixed-effects regression for Fourier amplitude spectra (0.2–30 Hz) of events spanning 2009–2023 (moment magnitude 1.8–6.5), with ∼434,000 recordings from 9948 events. By relaxing the ergodic assumption, we explicitly include site-, path-, and source-specific random effects and further introduce nonstationarity in time by allowing these effects to vary across three phases (presequence, main sequence, and postsequence). The residual components of the NENS model reveal significant spatiotemporal variations in ground motion. The net effect on median ground-motion predictions is a locally variable reduction in ground-motion amplitudes during the dissipative phases of the sequence (e.g., during fluid-saturated or highly scattering conditions) with respect to a stationary model. These findings underscore the importance of accounting for both spatial and temporal nonergodicity of ground-motion prediction in central Italy and demonstrate how high-density data in well-instrumented regions can be used to capture transient changes in source, path, and site effects.
Sgobba et al. (Wed,) studied this question.