## 3 Methods  
The continuous ambient vibrations collected at the Acuto field laboratory were processed following an approach based on the automatic extraction of energetic events and the preliminary classification of waveforms via manual inspection. This step is often considered paramount to constituting a reliable dataset of MS events and thus excluding false events from consideration. The flow chart of Figure 3 describes the adopted workflow. The automatic detection of MS signals embedded within ambient seismic noise recordings was performed using an STA/LTA triggering algorithm. Firstly, seismic recordings were preprocessed by applying automatic demeaning, detrending, and high-pass filtering above 2 Hz to remove any potential disturbances at very low frequencies. The STA/LTA algorithm was then launched on every file containing absolute acceleration values recorded from each channel of the array. This algorithm is one of the most exploited techniques for detecting seismic transients in various seismological contexts [51; 52], enabling the identification of energetic signals over ambient noise. The functioning principle of the STA/LTA trigger algorithms relies on the analysis of the ratio between the average amplitude of signals computed over two different moving windows. The STA (short-time average) measures the instant amplitude of the signal, and it is sensitive to transient energetic events, while the LTA (long-time average) quantifies the average amplitude of the background noise. The STA/LTA ratio is continuously computed and when it exceeds a user-defined triggering threshold an event is declared, and the related signal is registered until the ratio decreases to a detriggering level. Even though STA/LTA algorithms are among the most widely exploited techniques for the detection of seismic events, the calibration of the triggering threshold and the length of the moving windows represent major challenges. This calibration step should always be performed by considering the characteristics of the events and the characteristic background noise at the site under investigation [28]. In general, STA windows 2-3 times greater than the dominant period of the MS signal and LTA windows 10-100 times greater than the STA window size generally produce good quality results. This calibration step was performed through the preliminary analysis of training datasets aimed at defining the best tuning of STA/LTA parameters. For the scope of this study, the size of STA and LTA windows was set to 0.1 s and 10 s, with triggering and detriggering thresholds fixed to 4.  
must be shorter than a user-defined duration (T\({}_{\text{Th}}\)) that starts in correspondence with the first arrival of the MS event. When both conditions were satisfied, the event was extracted and further analyzed. In this study, the minimum number of triggered channels (N\({}_{\text{Th}}\)) to declare an event was set to 4, while the maximum time interval (T\({}_{\text{Th}}\)) was set to 0.15 s. It is worth specifying that the first condition (N\({}_{\text{Th}}\)) was found to be the most dominant in constraining the number of MS events detected by the STA/LTA algorithm. This threshold can vary from one up to the maximum number of channels, and the higher its level, the greater the coherence of signals and the reliability of MS events. In contrast, when dealing with low signal-to-noise ratio recordings, it is not rare to miss the detection of events due to different factors that can significantly affect monitoring arrays (e.g., the distance between sensors, the presence of fractures or potential malfunctioning) and a highly conservative threshold can lead to a potential loss in information. Using the aforementioned STA/LTA parameters, it was possible to build an MS dataset of 864 events. After the automatic event identification and extraction, a preliminary classification of the collected signals was performed to discriminate between MS and false events.