GNGTS 2022 - Atti del 40° Convegno Nazionale

96 GNGTS 2022 Sessione 1.2 STA/LTA automatic cut. The problem of detecting Seismo-volcanic events such as Explosion Quakes and their identification in the raw signal is a key problem when you need to collect data for future analysis in the seismo-volcanic field. With the continuous growth of data available over time, thanks to the expansion of the seismic networks, fulfilling this request by a human operator can result laborious and time-consuming. So, there is a need to use automatic methods for the extraction of the events. In Fenner et. al. , 2022, the authors try to solve this problem. In particular, they overcome the drawbacks of automatic collecting seismo-volcanic events in Stromboli volcano using a user-friendly approach called Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). AWESAM allows the generation of catalogues of seismo-volcanic events from recordings at a seismic station located in the proximity of a volcano. The catalogues contain information about the maximum amplitude, waveform, duration of the event and corresponding time of the seismo-volcanic events during a certain period. In the State-of-the-art there are many Volcano Seismic Recognition (VSR) systems that allow real-time monitoring and consistent catalogues. These VSR systems decrease their efficiency when used to recognize events from more than one station or related to different volcanoes. In Cortés et. al. (2021), the authors propose a Volcano-Independent VSR (VI.VSR) solution to generate labelled catalogues without the dependence on the volcanic context. In this work, we have implemented a system to perform the automatic cut of the Explosion Quakes (EQ) from raw signals collected by Dario Delle Donne from Osservatorio Vesuviano (OV) INGV, using STA/LTA algorithm. In particular, the idea is to carry out a statistical study to find the combination of STA and LTA window sizes that automatically cut at best the EQs, with respect to the cuts made by an expert operator. The window sizes of STA / LTA where to search are stored in two lists of values, sta_list = {1,2,3,..,10} and lta_list = {10,20,30,..,100}. Note that both are of 10 elements. Fig. 2a shows the case of three EQs, while Fig. 2b shows a detail on the last one. Fig. 2c reports the result of the STA/LTA algorithm on this EQ (bottom) and the trigger from STA/ Fig. 2 - (a) Multiple EQs, (b) Zoom on third EQ, (C) STA/LTA performed on third EQ. Red Bar is Trigger On and Blue Bar is Trigger Off.