## 4 Results
### Analysis and Preliminary Classification of the MS Dataset
The cluster of MS events is characterized by a frequency range spanning between 40 and 110 Hz, while the cluster of false events exhibits a bimodal distribution with two isolated local maxima located at very low (<10 Hz) and very high frequencies (>300 Hz). A synthesis of the eight parameters considered for comparing the identified classes of events is presented in Figure 8. As previously stated, the objective of this analysis was to produce a comparison between MS and false eventsthat could strengthen the outcomes of the manual classification through the investigation of characteristic time- and frequency-domain features. Based on the obtained results and considering MS and false events as two separate clusters, the relationship between event frequency and intensity was analyzed for both clusters according to the modified Gutenberg-Richter power law (Equation (5)). Due to the impossibility of locating event sources and estimating their magnitudes, these frequency-intensity curves were built considering the cumulative number of events having I\({}_{\rm A}\) values greater than a certain I\({}_{\rm A}\). Their b-values were computed through linear regression (Figure 9). From the obtained results, which can be considered to be statistically significant in virtue of the high values of R-square (\(\sim\)0.98), the two clusters are characterized by different frequency-intensity distributions having b-values of 0.69 (MS events) and 0.46 (false events). Figure 7: Results of the comparison between manually classified false and MS events based on time- and frequency-domain features. The following plots are presented: kurtosis vs. bracketed duration (**a**), kurtosis vs. uniform duration (**b**), maximum amplitude normalized to the mean of the envelope of the signal (A\({}_{\rm max}\)/A\({}_{\rm mean}\)) vs. Arias intensity (I\({}_{\rm A}\)) (\(\epsilon\)), A\({}_{\rm max}\)/A\({}_{\rm mean}\) vs. the frequency peak of power spectra (f\({}_{\rm max}\)) (**d**), and the 10-Hz frequency band with the maximum spectral content (f\({}_{\rm band}\)) (**e**). Although one of the main goals of reconstructing frequency-intensity distributions for earthquakes is the characterization of seismic sources and the failure mechanism, since different b-values can be considered as source-specific [38, 61], no inferences can be proposed here in terms of source or failure mechanism assessment. However, the  
Figure 8: Synthesis of the statistical comparison between the two manually identified classes of detected events. For each class, the extension of the blue boxes represents the interquartile range (IQR, defined as the difference between the upper (Q3) and lower (Q1) quartiles), red lines are median values, black whiskers extend to extreme values, and red crosses are the not-considered outliers. The parameters considered for this analysis are PGA (**a**), normalized amplitude (**b**), kurtosis (**c**), Arias intensity (**d**), bracketed (**e**) and uniform duration (**f**), peak frequency (**g**), and most energized frequency band (**h**). Figure 9: Comparison between the logarithm of Arias intensity (I\({}_{A}\)) and the logarithm of the cumulative number of occurrences (N) for MS events and false events, following the modified [PERSON] and [PERSON] (1954) power law relation. observed differentiation between frequency-intensity distributions of MS and false events is interpreted as an element that further corroborates the goodness of the manual classification process. Even though these results are insufficient to constrain and characterize the source of MS events, this analysis can be considered to be a preliminary attempt to gain valuable insights to distinguish possible source mechanisms responsible for the local microseismicity.