## 5 Discussion  
The availability of a vast dataset consisting of almost 5000 h of continuous ambient noise recordings allowed us to calibrate STA/LTA triggering algorithm parameters efficiently. Since several analyses had already been conducted on part of the here-discussed monitoring data [18; 23; 24], it was possible to optimize the event detection algorithm from the results obtained by [PERSON] [23]. In their framework, a total number of 115.000 events were detected employing the same STA/LTA algorithm with different settings (STA = 0.01 s, LTA = 30 s, STA/LTA = 4, and NTh = 2) over a period spanning from February 2018 to April 2019. The results obtained by [PERSON] [17] were considered valid in terms of the number of detections, but no manual or automatic event classification was performed on the extracted signals because it was considered not relevant for the final objective of their work. Although, when investigating potential correlations between MS activity and environmental factors (i.e., rainfalls and temperature variations), the consideration of false events could vitiate the outcomes, especially when they may represent the great majority of the dataset. For this reason, to reduce the number of detections within the considered monitoring periods and at the same time avoid significant losses in information, the STA/LTA algorithm settings were calibrated on a training dataset of several months. The most significant difference between the here-proposed parameters and those adopted in previous studies is represented by the size of the STA window and the selected value of NTh. In this study, they were increased to reduce the algorithm's sensitivity to very short and low-coherence signals. The semi-automatic approach adopted for the detection and classification of waveforms led to the construction of an MS dataset comprising 273 MS events over the 20-month monitoring period. As previously described, the monitoring dataset is affected by several issues, among which the most impactful are surely the presence of periodic time gaps in recordings, the limited number of detected MS events, and the impossibility to locate the events inside the rock mass. The lack of information concerning the genesis of MS events is derived from the poor geometry of the monitoring array which did not permit finding the location of their sources. Therefore, some uncertainties related to their origin still remain and cannot be completely neglected. Nevertheless, the statistical analysis performed on the manually identified classes of events allowed us to reliably consider the identified MS events as indicators of irreversible deformations deriving from fracturing processes of the rock mass. Based on the available data, the conducted analyses provided a deeper understanding of the role played by continuous near-surface temperature fluctuations and extreme thermal transients in influencing the stability of the fractured rock block. Although the limited number of event detections did not provide an opportunity to perform a robust statistical analysis between environmental stressors (e.g., rainfalls and temperature fluctuations) and microseismicity, some interesting inferences could still be derived from the obtained results. One of the most interesting outcomes that emerged from the comparison between the above-presented monitoring periods is probably the different distribution of MS events during the heating and cooling phases of the investigated rock mass. Although the analyzed dataset is also affected by numerous time gaps and by a limited number of MS events, the temporal distribution of MS activity registered in average climatic conditions (second and third periods--Figure 12d-i) is mostly concentrated in the cooling phases of the rock mass, contrary to what was observed during the winter storm of February 2018.