Reliability of source parameters for small events in central italy: Insights from spectral decomposition analysis applied to both synthetic and real data

We apply a spectral decomposition approach to isolate the source spectra from propagation and site effects and, in turn, to estimate the source parameters of small-to-moderate earthquakes that occurred in central Italy. The data set is composed of about 400,000 waveforms relevant to 4111 earthquakes in the moment magnitude range 1.5–6.5, recorded by a high-density network of stations installed in the study area. We first investigate the reliability of the source parameters for small magnitudes through numerical simulations. We generate synthetic spectra for different source scaling models and near-surface attenuation effects, considering the source–station geometry and the data availability of the central Italy data set. Our analysis with synthetics shows that the spectral decomposition is effective in isolating the source contributions from other factors. Moreover, the analysis of the residual distributions suggests that moment magnitude 1.8 is the lower bound for the retrieval of reliable Brune’s source parameters, although we observe an increase of residual’s variability below magnitude 3, and the estimated source parameters could be biased below magnitude 2.3. Remarkably, the assessment of the stress drop Δσ for small events is strongly hampered by site-specific attenuation near the surface. In view of the results with synthetics, we analyze the source parameters of earthquakes recorded in central Italy. The corner frequency versus seismic moment relationship describes a source scaling in which Δσ increases with increasing moment magnitude Mw, the mean Δσ varying from 0.1 MPa for Mw 5. In particular, Δσ increases mainly for Mw in the ranges 2.5–3 and 4.5–5.2. The corner frequencies estimated from the apparent source spectra do not show any dependence on hypocentral distance and magnitude, confirming that uncorrected anelastic attenuation effects do not significantly bias the results