DOI: 10.13127/etna/mavt2021
title: A dataset from a multi-station analysis of volcanic tremor at Mt. Etna, Italy, in 2021 (MAVT2021)
title: Dati da un'analisi multi-stazione del tremore vulcanico dell’Etna, Italia, nel 2021
title: MAVT2021

description: The dataset refers to seismic data analyses with a machine learning method in 2021, during which 52 lava fountain episodes occurred at Mt. Etna, Italy. These lava fountains were short-lived (a few hours-long) phenomena, which stemmed from the Southeast Crater, the youngest of the summit craters of the volcano. Each episode was preceded, accompanied, and followed by variations in the amplitude and frequency content of the background seismic radiation, the so-called volcanic tremor. In this perspective, we refined a machine learning analysis based on pattern classification, which was encompassed in the multi-station warning system by Spampinato et al. (2019). The system flags alerts exploiting the spectral characteristics of the volcanic tremor, continuously acquired by the stations of the Etna permanent seismic network. In its original configuration, which combines Self-Organizing Maps (SOM; Kohonen, 2001) and fuzzy clustering analysis (Zadeh, 1965), the system applied a voting scheme based on the number of stations and their weight for which alert criteria are met. Using the warning information of the original configuration, we exploited the application of thresholds to forecast higher levels of volcanic activity, from unrest to paroxysms. In doing so, we considered the values of the RGB (Red/Green/Blue) color code, which are the results of the pattern classification of the volcanic tremor data. In particular, we focused on the red color (R) which tends towards the value 1 (full red saturation) at the climax of each lava fountain.
Here, we provide the log files for the time span from January 1 to December 31, 2021, considering five threshold values of R, i.e., 0.50, 0.52, 0.55, 0.58, and 0.60. For each file we report: the UTC time (yyyymmdd_hh:mm) with increasing step of five minutes; the number of active stations; the value of the alert flag (0= no alert; 1=alert) for each station considered (for example, ECNE) which reached or topped a given value of R (for example, red>0.6).

Acknowledgments
This work was designed within the project IMPACT (A multidisciplinary Insight on the kinematics and dynamics of Magmatic Processes at Mt. Etna Aimed at identifying preCursor phenomena and developing early warning sysTems). IMPACT belongs to the Progetti Dipartimentali INGV [DIP7], https://progetti.ingv.it/index.php/it/progetti-dipartimentali/vulcani/impact#informazioni-sul-progetto.
description: Il dataset proposto è stato ottenuto mediante un metodo di “machine learning” riguardante l’analisi di dati sismici del 2021. Nel corso di quell’anno sono avvenuti 52 episodi di fontane di lava all’Etna, in Italia. Queste fontane hanno avuto breve durata (qualche ora) ed hanno preso origine dal Cratere di Sud-Est, il più giovane dei crateri sommitali del vulcano. Ciascun episodio è stato preceduto, accompagnato e seguito da variazioni nell’ampiezza e nel contenuto in frequenza della radiazione del segnale sismico di fondo, il cosiddetto tremore vulcanico. In questo contesto, abbiamo perfezionato un sistema di allertamento multistazione realizzato da Spampinato et al. (2019). Il sistema invia allerte utilizzando le caratteristiche spettrali del tremore vulcanico continuamente acquisito dalle stazioni della rete sismica permanente dell’Etna. Nella sua configurazione originale, che combina “Self-Organizing Maps” (SOM; Kohonen, 2001) e “fuzzy clustering” (Zadeh, 1965), il sistema applica uno schema di votazione basato sul numero di stazioni e sul peso (per ciascuna stazione) per il quale sono soddisfatti i criteri di allerta. Utilizzando l’informazione di allertamento della configurazione originale, abbiamo sfruttato l’applicazione di soglie per prevedere livelli più elevati di attività vulcanica, dalla condizione di “unrest” ai parossismi. Nel fare ciò, abbiamo considerato i valori del codice colore RGB (Rosso/Verde/Blu) risultanti dalla classificazione dei pattern dei dati del tremore vulcanico. In particolare, ci siamo concentrati sul colore rosso (R) che tende al valore 1 (saturazione rossa piena) al culmine di ogni fontana di lava.
Qui forniamo i “log files” per l’intervallo temporale dal 1 gennaio al 31 dicembre 2021, considerando cinque valori di soglia di R, ovvero 0.50, 0.52, 0.55, 0.58, and 0.60. Per ciascun file noi indichiamo: il tempo UTC (yyyymmdd_hh:mm) con passo di incremento di cinque minuti; il numero di stazioni attive; il valore dell’allertamento (0= nessuna allerta; 1=allerta) per ciascuna stazione considerata (per esempio, ECNE) che ha raggiunto o superato un dato valore di R (per esempio “red>0.6”).

Ringraziamenti
Questo lavoro è stato svolto nell’ambito del progetto IMPACT (A multidisciplinary Insight on the kinematics and dynamics of Magmatic Processes at Mt. Etna Aimed at identifying preCursor phenomena and developing early warning sysTems). IMPACT rientra fra i Progetti Dipartimentali INGV [DIP7], https://progetti.ingv.it/index.php/it/progetti-dipartimentali/vulcani/impact#informazioni-sul-progetto.
description: The continuous seismic data were analyzed by using the software KKAnalysis (Messina & Langer, 2011), which carried out the pattern classification with a step of five minutes. Following the procedures described by Langer et al. (2011) and by Spampinato et al. (2019), the results obtained were expressed as numerical values in the RGB color code mirroring the spectral characteristics of the seismic signal. In particular, we focused on the red color (R), considering the usage of thresholds to detect conditions leading to paroxysmal activity.
We considered five threshold values of R, namely 0.50, 0.52, 0.55, 0.58, and 0.60, and the number of stations where these values were reached or topped in the time span from January 1 to December 31, 2021. In that interval, 52 lava fountains occurred. From the files: multistation_analysis_20210101_20220101_red.050.log, multistation_analysis_20210101_20220101_red.052.log, multistation_analysis_20210101_20220101_red.055.log, multistation_analysis_20210101_20220101_red.058.log,  multistation_analysis_20210101_20220101_red.060.log, we obtained the so-called ‘Receiver Operation Characteristic’ (ROC; Metz, 1978) curves, which allowed us to establish rules for the identification of suitable criteria to evaluate the performance of our new warning system. This aim was achieved comparing the “true positives” (TP; events correctly flagged) with respect to “false positives” (FP; events flagged without having occurred). In-depth analyses of the ROC curves obtained and the overall interpretation of the results are discussed by Langer et al. (submitted to Scientific Reports,2022).
description: The volcanic tremor data considered in this study were continuously acquired by a group of broad-band seismic stations belonging to the permanent monitoring network run by INGV on Mt. Etna. The stations (ESVO, EPLC, ECNE, EPDN, ECPN, EMFO, ESLN, and ESPC) are installed within a maximum distance of about 8 km from the summit craters of Mt. Etna, in a rectangle with the top left corner in N37.79°, E14.93° and the lower right one in N37.68°, E15.11°. Each station is equipped with a Nanometrics Trillium™ seismometer having a natural period of 40 s. The dynamic range of the digitizer and the sampling rate are 24 bits and 100 Hz, respectively.