## 4 Results  
### Analysis and Preliminary Classification of the MS Dataset  
The results obtained using the STA/LTA algorithm are presented in Figure 5, where the raw number of triggered events is plotted. During the first monitoring campaign (February 2018-May 2018), 507 events were detected, while only 357 were extracted from the second campaign (November 2018-October 2019). From the observation of the detected events during the considered monitoring periods, different rates and trends (Figure 5a,b) in the occurrence of the events could be discerned. This evidence may be a consequence of the different array configurations employed during the two monitoring windows. By considering the different spacings between the two array configurations and the potential attenuation of seismic waves due to the presence of discontinuities, a dense array such as the one arranged from February 2018 to May 2018 may have the ability to detect a higher number of events common for different channels (Figure 5c,d). In Figure 5e, the distribution of the 864 triggered events is described in terms of the hour of the day in which they occurred. Interestingly, while the events belonging to the first monitoring campaign tend to be distributed almost homogeneously during the day, with no isolated peaks, the second monitoring campaign is characterized by a suspicious maximum between 11:00 and 12:00. This outcome may be in part biased by the almost complete absence of events during the night hours for the second monitoring period. In fact, as previously introduced, the monitoring system suffered from recurrent interruptions of the power supply from after sunset to some hours after sunrise (Figure 5f). Following the automatic extraction of signals from the continuous recordings, the derived dataset was classified through a manual screening to exclude false detections from the analysis of the local microseismicity. The manual classification was performed by observing waveforms, power spectra, and spectrograms of every array channel (Figure 6). This technique is widely considered one of the fastest and most accurate methods for concurrently analyzing the time- and frequency-domain characteristics of signals [21]. This analysis led to the identification of multiple recurrent types of events. For the aim of this work, the event classification was performed between only two classes: the first one clustered all evident disturbances that could not be considered as natural events, and the second one comprised all the signals potentially related to the stability conditions of the monitored rock mass and rock block. Differently from several authors [8; 21; 29], within the latter class no further classification was made because it was not considered essential for the objective of this analysis. Figure 5: MS dataset collected at the Acuto field laboratory during the two monitoring campaigns conducted between February 2018 and October 2019. Number of events per day (**a**), cumulative number of events (**b**), and number of events detected per accelerometers during the first (**c**) and second monitoring campaign (**d**). Distributions of the detected events (**e**) and of the \(1\) h recorded files (**f**) during both monitoring campaigns as a function of the hour of the day. Figure 6: Example of electrical disturbances recorded at the Acuto field laboratory and manually classified as false events (**a-c**); example of a manually classified single MS event (**d–f**); example of a manually classified sequence of MS events (**g–i**). The results of the manual classification highlighted that the MS monitoring network was affected by frequent electrical disturbances. These electrical transients simultaneously appear on all channels as single or multiple impulsive spike-like signals (Figure 6a-c) and are either characterized by a very broadband frequency content or by narrow frequency peaks systematically located above 200 Hz.