## 4 Discussion and Conclusions  
To automate the detection and classification of seismic events, methodologies based on neural networks or even the use of time domain templates are often used to identify seismic events in the recorded signals. In the present work, we proposed a detection methodology based on the parametrization of spectral shape and coherence of the seismic signals among selected stations. The procedure, tuned for low-frequency earthquakes, was applied to continuous signals recorded at Mt. Vesuvius over a 28-month time span. The results highlighted 80 signal windows with spectral features compatible with LF events. For about \begin{table}
\begin{tabular}{c c c} \hline
**n.** & **(yyyymmdd hh:MM s)** & **Seismic Signal Type** \\ \hline
1 & 2019 01 29 10:49 32.66 & LF seismic event \\ \hline
2 & 2019 11 25 00:01 31.55 & LF Swarm \\ \hline
3 & 2020 05 02 02:10 58.33 & LF seismic event \\ \hline
4 & 2020 05 02 02:12 14.01 & LF seismic event \\ \hline
5 & 2020 09 07 19:23 12.22 & LF seismic event \\ \hline
6 & 2020 09 07 19:32 34.03 & LF seismic event \\ \hline
7 & 2020 09 07 19:33 55.25 & LF seismic event \\ \hline
8 & 2020 11 16 05:58 24.58 & Two LF seismic events \\ \hline
9 & 2020 11 16 05:59 39.68 & LF seismic event \\ \hline
10 & 2020 12 04 23:05 35.47 & LF seismic event \\ \hline
11 & 2021 04 02 15:29 46.04 & LF Swarm \\ \hline
12 & 2021 04 02 15:30 33.85 & LF Swarm \\ \hline \end{tabular}
\end{table} 
Table 1: LF seismic events detected with the proposed procedure applied to continuous seismic signals recorded at Mt. Vesuvius over a 28-month time span. Figure 10: Spectrogram of 202009071932 LF seismic event evaluated on north–south component of BKSG seismic station. 12 of these events (Table 1), those with the higher amplitude and signal-to-noise ratio, the filtered seismograms allowed the recognition of P-S wave pairs that permit a rough location and unambiguous classification of these events as deep LF earthquakes, very similar to those reported by [PERSON] and [PERSON] [12]. In contrast, very low amplitude events are visible with a low signal-to-noise ratio only in filtered signals recorded at the lowest noise sites. Although they have spectral characteristics compatible with LF events, we could not provide a reliable classification because the lack of identifiable P-S wave pairs prevents any possible location of the source. These events constitute the majority of the detected signals (more than 80%). The hypothesis of an endogenous source is very reasonable because these events are visible only at the sites in the central area of Vesuvius and not at the seismic stations around the volcano, at a higher distance from the crater. A better investigation of these seismic events would be feasible with higher quality data, such as dense array recordings [24] or deep borehole recordings. In summary, the results of our analysis show that:  
1. The joint use of spectral parameters and coherence applied to a few selected stations of the seismic monitoring network operating at Mt. Vesuvius, which are characterized by low background noise, allowed for the detection of signals classifiable as LF earthquakes or LF tremors. 2. The detected LF events reveal an amount of deep LF seismicity at Vesuvius much larger than that reported in the catalog. 3. The applied procedure also detects a large number of low-amplitude events hidden in the background noise that have the characteristics of LF events, but their effective classification requires further investigation and possibly higher quality seismic data. 4. The overall outcomes suggest that the proposed procedure could be appropriate for real-time applications aimed at LF signal detection, but its suitability must be further investigated. The analysis procedure applied in this work is strongly focused on the detection of LF events characterized by spectral features in narrow ranges, as observed for events detected in the past years at Vesuvius.