## 4 Discussion and Conclusions
The ML-based models provide very reliable solutions when all the traces of all the volcanoes are mixed together, allowing the identification of pieces of background tremor. But more than this, our method ascribes each recognized tremor to the relative volcano with its own characteristics. In addition, introducing new classes of labeled noise waveforms in the framework of ML techniques allows enriching the training phases so that the classifier can better discriminate among low-energy volcanic signals from spurious ones, just reducing the probability to detect false positive events [35]. The accurate identification and classification of continuous seismic noise represent progress toward the characterization of background signals, which can also be implemented in real-time for monitoring purposes. The promising performance shown by our ML approach in background noise analysis applied to volcanoes with different activities (e.g., hydrothermal, volcanic, anthropogenic, etc. ), in quiescent or in unrest phases, and with even different geological features (calderas, stratovolcano), makes us confident that the method can be extended to a large variety of volcanoes for extracting their fingerprints and significantly contributing to the study of the seismic/volcanic sources. Separate classification of background noise and transients opens the way to hybrid approaches that use specific classifiers, integrated into decision models to employ both signals, even constructed from a dataset provided by a single seismic station. This would be successful in those areas that are not accessible to locate seismic networks or seismic arrays. Finally, our network should recognize any anomalies in the background noise, thus indirectly evidencing changes in the dynamics of a volcano. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12146835/s1](https://www.mdpi.com/article/10.3390/app12146835/s1)]([https://www.mdpi.com/article/10.3390/app12146835/s1](https://www.mdpi.com/article/10.3390/app12146835/s1)), Figure S1: Examples of seismic noise three-component (Z = vertical, N = north-south, E = east-west) seismograms and corresponding spectrograms recorded at stations ABB2, AMS2, TAGG, and BGNG located at Campi Flegrei. Figure S2: Examples of seismic noise three-component (Z = vertical, N = north-south, E = east-west) seismograms and corresponding spectrograms recorded at stations IOCA, T1363, and T1364 located at Ischia. Figure S3: Examples of seismic noise three-component (Z = vertical, N = north-south, E = east-west) seismograms and corresponding spectrograms recorded at stations T1361, T1362, and T1365 located at Ischia. Figure S4: Examples of seismic noise three-component (Z = vertical, N = north-south, E = east-west) seismograms and corresponding spectrograms recorded at stations BKWG, BKSG, and FAL2 located at Vesuvius. Figure S5: Examples of seismic noise three-component (Z = vertical, N = north-south, E = east-west) seismograms and corresponding spectrograms recorded at stations COCA, COBA, and COME located at Colima. Conceptualization, M.F. ; methodology, D.R.-Y. and S.S.; investigation, D.R.-Y. and S.P. ; data curation, S.P. ; writing--original draft preparation, M.F. and S.P. ; writing--review and editing, E.D.L., M.F., D.R.-Y., S.P. and S.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Acknowledgments:** We acknowledge the \"Istituto Nazionale di Geofisica e Vulcanologia, Sez. Napoli--Osservatorio Vesuviano\" for providing the seismic dataset of Ischia, Campi Flegrei, and Vesuvius. We thank the \"Istituto Nazionale di Geofisica e Vulcanologia (INGV)-Sez. Osservatorio Vesuviano\", the \"Observatorio Vulcanologico de Colima of Colima University (OVC)\" and the \"Instituto Andaluz de Geofisica (IAG)\", University of Granada, Spain for the seismic data of the Colima volcano. **Conflicts of Interest:** The authors declare no conflict of interest.