## 5 Discussion and Conclusions  
The Solfatara-Pisciarelli area of the Campi Flegrei caldera has recently shown episodes of a sudden increase in hydrothermal activity and mud and gas emissions. During these episodes, Short Duration Events (SDEs), related to the intensity of mud pool boiling, were recorded in the fumarolic tremor. Identifying such events can help us better define the volcano's state of activity. For this purpose, we have proposed a novel method to extract the features from seismic tremor data generated by the fumarolic-mud pool system of Pisciarelli (central area of the Campi Flegrei caldera) and we have applied an unsupervised neural network (SOM) to cluster two different types of seismic signals, namely the fumarolic tremor with SDEs and the typical fumarolic tremor (without SDEs). The SOM was mainly employed in this application to test how well the chosen coding for the data was able to discriminate between them. The next aim will be to use this parameterization for the creation of an automatic classification system of these two types of tremors using a supervised neural technique. The Self-Organizing Map (SOM) is a powerful tool for data analysis exploited for several purposes. Its main advantage is that the SOM is able to cluster large, high-dimensional and complex datasets [51]. Moreover, it provides an easily understandable data representation. The reduction of dimensionality and grid clustering makes it simple to observe similarities in the data. The main drawback of SOM is that it needs necessary and sufficient data to obtain meaningful clusters. Weight vectors must be based on features that characterize the inputs well so that they can be effectively grouped and classified. A lack of data or inappropriate data in the weight vectors will degrade the performance of the method. Our feature extraction method is based on the downsampling of the sorted envelope and the downsampling of the smoothed spectrum of 1 min segments of continuous fumarolic tremor signals. This novel method for feature extraction allows a good encoding of thesignals and an efficient clustering with the SOM, which effectively separates the typical fumarolic tremors from the tremors with SDEs. The analysis of the temporal evolution of the clusters allowed us to recognize the onset of episodes of fumarolic tremors with SDEs, considered indicators of an anomalous increase in hydrothermal activity [15,22]. The occurrence of SDEs was considered linked to phases of intensification of hydrothermal activity because this type of tremor was accompanied by the expansion of the gas and mud emission areas at Pisciarelli during the episode that occurred in 2018 and could culminate in geyser-like phreatic activity. Therefore, the method we propose can improve the monitoring of the hydrothermal activity of the Campi Flegrei caldera, which is showing escalating anomalies. A.M.E., W.D.C., G.M. and F.G. conceptualization, dataset preparation, article writing, data analysis procedures, interpretation of results, article revising. All authors have read and agreed to the published version of the manuscript. This work was supported by the Progetto ORME, INGV Project \"Pianeta Dinamico\"--Working Earth (CUP 1466 D53J19000170001--\"Fondo finalizzato al rilancio degli investimenti delle 1467 amministrazioni centrali dello Stato e allo sviluppo del Paese\", legge 145/2018). The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation (contact persons: [PERSON], email [EMAIL_ADDRESS] and [PERSON], email [EMAIL_ADDRESS]). This work was partially supported by the project Progetto Strategico Dipartimentale INGV 2019. \"Linking surface Observables to sub-Volcanic plumbing-system: A multidisciplinary approach for Eruption forecasting at Campi Flegrei caldera (Italy)\". G.M.