In this study, microseism recordings from a near coast seismic station and concurrent significant sea wave heights ($$H_{\frac{1}{3}}$$) are analysed to calibrate an empirical relation for predicting sea wave height in the Ligurian Sea. The study stems from the investigation of the damaging sea storms occurred in the Ligurian Sea between 2008 October and November. Analysing data collected in this time frame allows identification of two types of microseism signal, one associated to the local sea wave motion and one attributable to a remote source area. The former is dominated by frequencies greater than 0.2 Hz and the latter by frequencies between 0.07 and 0.14 Hz. Moreover, comparison of microseism spectrogram and significant sea wave heights reveals a strong correlation in that the spectral energy content of microseism results proportional to the sea wave height observed in the same time window. Hence, an extended data set including also observations from January to December 2011 is used to calibrate an empirical predictive relation for sea wave height whose functional form is a modified version of the classical definition of $$H_{\frac{1}{3}}$$. By means of a Markov chain Monte Carlo algorithm we set up a procedure to investigate the inverse problem and to find a set of parameter values for predicting sea wave heights from microseism.

In this study, we analyzed 10 yrs of seismicity in central Italy from 2008 to 2017, a period witnessing more than 1400 earthquakes in the magnitude range $2.5≤Mw≤6.5$. The data set includes the main sequences that have occurred in the area, including those associated with the 2009 $Mw$ 6.3 L'Aquila earthquake and the 2016–2017 sequence ($Mw$ 6.2 Amatrice, $Mw$ 6.1 Visso, and $Mw$ 6.5 Norcia earthquakes). We calibrated a local magnitude scale, investigating the impact of changing the reference distance at which the nonparametric attenuation is tied to the zero‐magnitude attenuation function for southern California. We also developed an attenuation model to compute the radiated seismic energy ($Es$) from the time integral of the squared ground‐motion velocity. Seismic moment ($M0$) and stress drop ($Δσ$) were estimated for each earthquake by fitting a $ω$‐square model to the source spectra obtained by applying a nonparametric spectral inversion. The $Δσ$‐values vary over three orders of magnitude from about 0.1 to 10 MPa, the larger values associated with the mainshocks. The $Δσ$‐values describe a lognormal distribution with mean and standard deviation equal to $log(Δσ)=(−0.25±0.45)$ (i.e., the mean $Δσ$ is 0.57 MPa, with a 95% confidence interval from 0.08 to 4.79 MPa). The $Δσ$ variability introduces a spread in the distribution of seismic energy versus moment, with differences in energy up two orders of magnitudes for earthquakes with the same moment. The variability in the high‐frequency spectral levels is captured by the local magnitude ($ML$), which scales with radiated energy as $ML=(−1.59+0.52logEs)$ for $logEs≤10.26$ and $ML=(−1.38+0.50logEs)$ otherwise. As the peak ground velocity increases with increasing $Δσ$, local and energy magnitudes perform better than moment magnitude as predictors for the shaking potential. The availability of different magnitude scales and source parameters for a large earthquake population will help characterize the between‐event ground‐motion variability in central Italy.

We present Rapid Assessment of MOmeNt and Energy Service (RAMONES), a service for disseminating through a web interface, the estimates of seismic moment (⁠ $M0$⁠) and radiated energy (⁠ $ER$⁠) for earthquakes occurring in central Italy with local magnitudes above 1.7. The service is based on a fully‐automatic procedure developed for downloading and processing open seismological data from the European Integrated Data Archive, Italian Civil Protection repository, and Incorporated Research Institutions for Seismology (IRIS). In its actual configuration, RAMONES uses the seismic catalog generated through the event webservice of the Italian Institute of Geophysics and Volcanology (compliant with International Federation of Digital Seismograph Networks standards) to guide the data download. The concept of RAMONES is to estimate $M0$ and $ER$ from features extracted directly from recordings, namely the S‐wave peak displacement (⁠ $PDS$⁠) and the integral of the squared velocity (⁠ $IV2S$⁠) evaluated over the S‐wave window at local distances. A data set composed of 6515 earthquakes recorded in central Italy between 2008 and 2018 was used to calibrate the attenuation models relating $M0$ to $PDS$ and $ER$ to $IV2S$⁠, including station corrections. The calibration values for $M0$ and $ER$ were extracted from the source spectra obtained by applying a decomposition approach to the Fourier amplitude spectra known as the generalized inversion technique. To test the capabilities of RAMONES, we validate the attenuation models by performing residual analysis over about 60 earthquakes occurring in 2019 that were used for the spectral decomposition analysis but not considered in the calibration phase. Since January 2020, a testing operational phase has been running, and RAMONES has analyzed about 800 earthquakes by September 2020. The distribution of the source parameters and their relevant scaling relationships are automatically computed and disseminated in the form of maps, parametric tables, figures, and reports available through the RAMONES web interface.