## 4 Discussion and Conclusions  
In the framework of forecasting volcanic eruptions, much attention is generally devoted to the analysis of seismic transients for a prompt detection and cataloging of the waveforms. The reason is the strict link between the dynamical state of a volcano and the seismic signals such as LPs or VTs. On the other hand, seismic acquisition is a complex superposition of several signals emitted by different sources, which can be internal or external to a volcano, that can share common properties, especially the frequency content, making the discrimination even more difficult. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{6}{c}{**MLP**} \\ \cline{3-8}  & & **Train Acc** & **Train Loss** & **Test Acc** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{Exp. 1} & **AVG** & 95.424 & 0.179 & 94.622 & 0.948 & 0.946 & 0.946 \\ \cline{2-8}  & **STD** & 0.675 & 0.018 & 0.696 & 0.006 & 0.008 & 0.008 \\ \hline \multirow{2}{*}{Exp. 2} & **AVG** & 93.978 & 0.234 & 93.123 & 0.929 & 0.923 & 0.925 \\ \cline{2-8}  & **STD** & 0.720 & 0.017 & 0.611 & 0.006 & 0.008 & 0.005 \\ \hline \multirow{2}{*}{Exp. 1} & **Train ACC** & **Train Loss** & **Test ACC** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{Exp. 1} & **AVG** & 99.588 & 0.019 & 99.417 & 0.990 & 0.990 & 0.990 \\ \cline{2-8}  & **STD** & 0.399 & 0.009 & 0.399 & 0.000 & 0.000 & 0.000 \\ \hline \multirow{2}{*}{Exp. 2} & **AVG** & 95.648 & 0.118 & 95.229 & 0.970 & 0.951 & 0.954 \\ \cline{2-8}  & **STD** & 3.774 & 0.089 & 3.748 & 0.017 & 0.037 & 0.034 \\ \hline \end{tabular}
\end{table} 
Table 4: Average accuracy, precision, recall, and F1 score of the two models, evaluated for the two experiments. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 1} & **Experiment 01** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 2} & **Experiment 01** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 01** & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 1} & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 2} & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 2} & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 3} & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \hline \multirow{2}{*}{Exp. 4} & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \cline{2-8}  & **Experiment 02** & **MLP** & **CNN** \\ \hline \end{tabular}
\end{table} 
Table 5: Normalized confusion matrices for the MLP and CNN models for the two experiments. Here, we demonstrate that it is possible to recognize the fingerprint of a specific volcano by basically analyzing the background seismic noise, taking advantage of continuous acquisition in a stationary volcanic phase. This puts on a more quantitative basis the now consolidated idea that persistent tremor, both volcanic and hydrothermal, and some volcanic transients (i.e., LPs or explosion quakes) are generated by the same physical mechanism, eventually in different regimes involving different levels of seismic energy. Investigations were conducted by using ML techniques for the Neapolitan volcanoes and the Colima volcano. These analyses are advantageous in cases of weak ground motions as the background seismic noise, which is plenty of information often neglected by traditional signal processing techniques.