###### Abstract  
Mt. Vesuvius is a high-hazard active volcano surrounded by a densely populated area. Since human activities generate high levels of seismic noise, recognizing low-amplitude seismic events in the signals recorded by the local seismic monitoring network operating at Vesuvius is very difficult. Here, we describe an automatic procedure applied to continuous data with the aim of finding low-amplitude-low-frequency events hidden in the recorded signals. The methodology is based on the computation of two spectral parameters, central frequency \(\Omega\) and shape factor \(\delta\), at selected sites, and the coherence of the seismic signal among different sites. The proposed procedure is applied to 28 months of recordings from 2019 to 2021, tuning the search parameters in order to find low-frequency signals similar to those occasionally observed in the past at the same volcano. The results allowed us to identify 80 seismic events that have the spectral features of low-frequency earthquakes or tremor. Among these, 12 events characterized by sufficiently high signal-to-noise ratio have been classified as deep low-frequency earthquakes, most of which are not reported in the catalog. The remaining events (more than 60) are characterized by similar spectral features but with an extremely low amplitude that prevents any reliable location of the source and definitive classification. The results of this work demonstrate that the low-frequency endogenous activity at Mt. Vesuvius volcano is more frequent that previously thought. low-frequency earthquakes; signal detection; volcano seismicity; spectral parameters; coherence analysis +
Footnote †: journal: Medical  
methodologies [6]. Active volcanoes are characterized by a variety of seismic events that produce signals which differ in amplitude, waveform and spectral content. The most common are volcanic tremor, low-frequency (LF) earthquakes, long-period (LP) events, hybrid events, and volcano tectonic (VT) earthquakes [7; 8; 9; 10]. In this work, we refine and test an automatic detection procedure to identify LF earthquakes in the continuous seismic data recorded at Mt. Vesuvius by the seismic monitoring network managed by INGV-Osservatorio Vesuviano. The seismicity of Mt. Vesuvius is characterized by VT earthquakes of small magnitude (M\({}_{\rm D}\) < 3), mostly located at depths between 0 and 2 km below sea level (b.s.l.) and sometimes occurring as low-energy swarms. During the last decade, the average rate of this seismicity has been of about 900 VT earthquakes per year [11]. LF events characterized by low amplitude and deeper localization than VT earthquakes (between 5 and 7 km depth) have been identified in the seismic signals, sometimes as single sporadic events and in other cases as volcanic tremor consisting of low-frequency swarms [12]. The source of this LF seismicity was studied in another work [13], where the authors hypothesized the involvement of fluids in the genesis of these deep earthquakes. Over the past 10 years, the use of high-dynamic instrumentation, broadband sensors, and seismic arrays for surveillance, monitoring and research purposes has permitted the collection of high-quality data appropriate for automatic analysis. This facilitated the development of effective machine-learning techniques for analysis and classification of seismic signals recorded in active volcanic areas. For instance, an unsupervised technique based on a neural network approach for the detection and classification of seismic signals recorded at Mt. Vesuvius is provided by [PERSON] et al. [14]. Previously, [PERSON] et al. [15] provided automatic classification of seismic signals for the same volcano through neural networks. They introduced a new method for feature extraction, based on the combination of a waveform parametrization in the time domain, and an LPC (linear prediction coding) algorithm that provides a compressed and robust representation of the data in the frequency domain.