## 3 Methods
This stage of the analysis always represents a major task in MS monitoring, and it becomes crucial when potential correlations between environmental factors and microseismicity must be investigated. To this aim, a preliminary classification of signals was performed through the manual inspections of waveforms, power spectra, and spectrograms of every detected event. Such an analysis allowed us to concurrently investigate the coherency of time- and frequency-domain characteristics of signals over all of the channels of the array, leading to a significant reduction in the number of events (Figure 4). Figure 4: Example of one event automatically detected and classified as an MS event through visual screening of its time- and frequency-domain features (left: signal waveform; center: power spectral density estimate; right: spectrogram). To strengthen the results of the manual classification and to reduce the influence of subjectivity on the analysis, a statistical comparison between time- and frequency-domain characteristics of MS and false events was performed. This task is generally conducted by implementing fully or semi-automatic procedures that rely on machine learning algorithms--such as hidden Markov models (HMMs) [53], support vector machines (SVMs) [54], linear discriminant analysis (LDA) [8], fuzzy logic (FL) [55], random forest (RF) [56], convolutional neural networks (CNNs) [57], and k-means clustering [21]--to identify recurrent classes of events. For this study, a preliminary attempt was made to strengthen the affordability of the manual screening procedure. The whole dataset was investigated by analyzing multiple time- and frequency-domain parameters of the manually classified events following the workflow proposed by [PERSON] [21]. In the time domain, signals were analyzed by extracting information on their duration, shape, and energy content. Concerning their shape, three parameters were considered: the maximum amplitude (A\({}_{\text{max}}\) or PGA--peak ground acceleration), the normalized amplitude (A\({}_{\text{max}}\)/A\({}_{\text{mean}}\)), and the kurtosis of the signal envelope. Concerning their duration, bracketed and uniform durations were computed. The bracketed duration was retrieved as the time interval comprised between the first and the last exceedance of the signal over a fixed threshold. In contrast, the uniform duration is defined as the sum of all time intervals in which the signal exceeds the same threshold [58]. The Arias intensity (I\({}_{\text{A}}\)) was also calculated to evaluate the energy content associated with every event. This parameter is a measure of the cumulative intensity of ground motion obtained as the integral of the squared acceleration of the seismic signal:  
\[\text{I}_{\text{A}}=\frac{\pi}{2\text{g}}\int_{0}^{\text{t}_{\text{max}}} \text{a}(\text{t})^{2}\text{ dt} \tag{1}\]  
where a(t) is the ground acceleration at time t and t\({}_{\text{max}}\) is the total duration of the seismic signal. In addition, the maximum frequency of the Fourier power spectra (f\({}_{\text{max}}\)) and the total amount of spectral energy in 10 Hz frequency bands (f\({}_{\text{band}}\)), spanning from 0 to 1000 Hz, were extracted to characterize the spectral features of MS and false events. In particular, the spectral energy in each frequency band was computed as:  
\[\text{E}_{\text{band}_{0}\to f_{0}+10}=\int_{\text{f}}^{f_{0}+10}\text{ DFT(f) df} \tag{2}\]  
where DFT(f) is the value of the power spectrum at the frequency f, obtained through the discrete Fourier transform of the event signal. Another important task that should be performed when dealing with MS analysis is related to the location of event sources. For what concerns this work, event location was not conducted because it was found to be not possible for different reasons. Since the two adopted array configurations were organized in a very restricted space, no delays in the arrival time of MS events were identified on the different channels, even considering the high sampling frequency adopted (i.e., 2400 Hz).