The April-May 2006 volcano-tectonic events at Stromboli volcano (Southern Italy) and their relation with the magmatic system.

Between April 10th and May 22th 2006, a small seismic swarm of 5 volcano-tectonic events occurred on the volcanic island of Stromboli (Southern Italy). Two of these, having M > 3 and an intensity of about V-VI MCS, were clearly felt causing concern in the population. They were recorded during a period of increased explosive activity and were followed by two major explosions at the summit craters on May 22th, few hours after the last earthquake and on 16th June. The location of such events has been performed using a probabilistic approach based on the Equal Differential Time tecnique. Using this tecnique, we were able to locate all the events, showing how they cluster below the volcanic edifice at a depth of about 5÷6 km. From observed P wave polarities we determined the focal mechanisms of the 4 major events. Using earthquake scaling nlaws, we calculated the fault area and the average slip for the two major events. Finally, assuming an homogeneous half-space model we computed the isotropic stress changes below the volcano edifice. The negative stress variation over the central axis of the volcano suggests that the earthquakes were triggered by a pressurization of the magmatic system

IL SISTEMA DI ANALISI AUTOMATICA DEI SEGNALI SISMICI VLP DELLO STROMBOLI

Nel corso degli anni ‘90 l’uso di reti sismiche broadband in aree vulcaniche attive ha permesso di osservare in numerosi casi, segnali VLP (Very Long Period), ovvero segnali transienti con periodo dominante nella banda 2-50 s. Lo Stromboli, che con la sua persistente attività è un generatore di segnali VLP, è uno dei pochi vulcani su cui opera una rete sismica estesa costituita da stazioni broadband. A partire dal maggio 2003, è attivo presso la sede INGV Osservatorio Vesuviano un sistema automatico, denominato EOLO, per il rilevamento, la localizzazione e l’analisi in tempo reale di questi segnali. Il sistema EOLO riceve in ingresso (via internet) i segnali sismici registrati dalla rete broadband INGV dello Stromboli e fornisce, attraverso un’interfaccia web, sia i dati relativi ai singoli eventi VLP che delle statistiche giornaliere, mensili e annuali. L’interfaccia web interagisce con 3 database diversi: quello delle “forme d’onda”, il “catalogo eventi” e il database “statistiche”. Il database “forme d’onda” è costituito da un insieme di file in formato SAC, creati a partire dai segnali “grezzi” ricevuti in input. Il “catalogo eventi” rappresenta il cuore di tutto il sistema ed è implementato mediante SQL. Per ciascun evento VLP individuato, vengono determinati i parametri ipocentrali e le ampiezze alle varie stazioni e vengono inserite nel database “catalogo eventi”. Con periodicità oraria, viene aggiornato il database “statistiche”, costituito da grafici con gli andamenti orari e giornalieri del numero di eventi, della loro intensità e dell’andamento medio della polarizzazione dei segnali sismici VLP. L’interfaccia web consente di visualizzare, attraverso applet Java e script CGI, la localizzazione di ciascun evento, le forme d’onda, spettri e spettrogrammi, ed altre informazioni ritenute utili. Il sistema di rilevamento/localizzazione, che costruisce il database “catalogo eventi” è basato sull’analisi della coerenza delle forme d’onda VLP registrate alle varie stazione. Un calcolatore parallelo, basato su un cluster di 64 processori, esegue in tempo reale l’analisi della funzione semblance (indicativa della coerenza) su una griglia di dimensioni 8 km x 8 km x 2 km a spaziatura regolare 100 m x 100 m x 50 m, centrata sullo Stromboli. L’accadimento di un evento VLP produce il superamento di un valore di soglia della funzione semblance. La posizione del valore massimo della funzione semblance, durante un evento, è assunta come localizzazione. Nei prossimi mesi al sistema esistente sarà aggiunto un modulo per l’inversione della funzione sorgente dei singoli eventi VLP

A Neural-based Algorithm for Landslide Detection at Stromboli Volcano: Preliminary Results.

This study presents a neural-based algorithm for the automatic detection of landslides on Stromboli volcano (Italy). It has been shown that landslides are an important short-term precursor of effusive eruptions of Stromboli. In particular, an increase in the occurrence rate of landslides was observed a few hours before the beginning of the February 2007 effusive eruption. Automating the process of detection of these signals will help analysts and represents a useful tool for the monitoring of the stability of the Sciara del Fuoco flank of Stromboli volcano. A multi-layer perceptron neural network is here applied to continuously discriminate landslides from other signals recorded at Stromboli (e.g., explosion quakes, tremor signals), and its output is used by an automatic system for the detection task. To correctly represent the seismic data, coefficients are extracted from both the frequency domain, using the linear predictive coding technique, and the time domain, using temporal waveform parameterization. The network training and testing was carried out using a dataset of 537 signals, from 267 landslides and 270 records that included explosion quakes and tremor signals. The classification results were 99.5% predictive for the best net performance, and 98.7% when the performance was averaged over the different net configurations. Thus, this detection system was effective when tested on the 2007 effusive eruption period. However, continuing investigations into different time intervals are needed, to further define and optimize the algorithm

AUTOMATIC ANALYSIS OF SEISMIC DATA BY USING NEURAL NETWORKS: APPLICATIONS TO ITALIAN VOLCANOES.

The availability of the new computing techniques allows to perform advanced analysis in near real time, improving the seismological monitoring systems, which can extract more significant information from the raw data in a really short time. However, the correct identification of the events remains a critical aspect for the reliability of near real time automatic analysis. We approach this problem by using Neural Networks (NN) for discriminating among the seismic signals recorded in the Neapolitan volcanic area (Vesuvius, Phlegraean Fields). The proposed neural techniques have been also applied to other sets of seismic data recorded in Stromboli volcano. The obtained results are very encouraging, giving 100% of correct classification for some transient signals recorded at Vesuvius and allowing the clustering of the large dataset of VLP events recorded at Stromboli volcano

Models for Identifying Structures in the Data: A Performance Comparison

This paper reports on the unsupervised analysis of seismic signals recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset is composed of earthquakes and false events like thunders, man-made quarry and undersea explosions. The Stromboli dataset consists of explosion-quakes, landslides and volcanic microtremor signals. The aim of this paper is to apply on these datasets three projection methods, the linear Principal Component Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear Component Analysis (CCA), in order to compare their performance. Since these algorithms are well known to be able to exploit structures and organize data providing a clear framework for understanding and interpreting their relationships, this work examines the category of structural information that they can provide on our specific sets. Moreover, the paper suggests a breakthrough in the application area of the SOM, used here for clustering different seismic signals. The results show that, among the three above techniques, SOM better visualizes the complex set of high-dimensional data discovering their intrinsic structure and eventually appropriately clustering the different signal typologies under examination, discriminating the explosionquakes from the landslides and microtremor recorded at the Stromboli volcano, and the earthquakes from natural (thunders) and artificial (quarry blasts and undersea explosions) events recorded at the Vesuvius volcano

Neural analysis of seismic data: applications to the monitoring of Mt. Vesuvius

The computing techniques currently available for the seismic monitoring allow advanced analysis. However, the correct event classification remains a critical aspect for the reliability of real time automatic analysis. Among the existing methods, neural networks may be considered efficient tools for detection and discrimination, and may be integrated into intelligent systems for the automatic classification of seismic events. In this work we apply an unsupervised technique for analysis and classification of seismic signals recorded in the Mt. Vesuvius area in order to improve the automatic event detection. The examined dataset contains about 1500 records divided into four typologies of events: earthquakes, landslides, artificial explosions, and “other” (any other signals not included in the previous classes). First, the Linear Predictive Coding (LPC) and a waveform parametrization have been applied to achieve a significant and compact data encoding. Then, the clustering is obtained using a Self-Organizing Map (SOM) neural network which does not require an a-priori classification of the seismic signals, groups those with similar structures, providing a simple framework for understanding the relationships between them. The resulting SOM map is separated into different areas, each one containing the events of a defined type. This means that the SOM discriminates well the four classes of seismic signals. Moreover, the system will classify a new input pattern depending on its position on the SOM map. The proposed approach can be an efficient instrument for the real time automatic analysis of seismic data, especially in the case of possible volcanic unrest

Sistemi di trasmissione WiFi per il monitoraggio sismico del Vesuvio

First-year engineering students at the University of Queensland used an interactive webbook to acquire information skills. These helped them search information resources for their projects, which they are required to undertake as part of the subject Introduction to professional engineering. The information skills exercise was an integral part of the project and worth 10% of the overall assessment. The exercises were only available on the Web, allowing the students to enter their answers from home or wherever they had access to the Internet. All answers were marked automatically using a database of all possible answers. Students were able to go back to check their answers. Students were assessed on both their responses to the exercises and also their final bibliography which largely reflected the impact of the webbook. The entire process was evaluated. This paper presents the process and the outcomes of the first-year engineering project involving use of WWW for information skills instruction. The webbooks can be found at http://www.library.uq.edu.au/9e105/

Variable magnitude and intensity of strombolian explosions: Focus on the eruptive processes for a first classification scheme for Stromboli volcano (Italy)

Strombolian activity varies in magnitude and intensity and may evolve into a threat for the local populations living on volcanoes with persistent or semi-persistent activity. A key example comes from the activity of Stromboli volcano (Italy). The “ordinary” Strombolian activity, consisting in intermittent ejection of bombs and lapilli around the eruptive vents, is sometimes interrupted by high-energy explosive events (locally called major or paroxysmal explosions), which can affect very large areas. Recently, the 3 July 2019 explosive paroxysm at Stromboli volcano caused serious concerns in the local population and media, having killed one tourist while hiking on the volcano. Major explosions, albeit not endangering inhabited areas, often produce a fallout of bombs and lapilli in zones frequented by tourists. Despite this, the classification of Strombolian explosions on the basis of their intensity derives from measurements that are not always replicable (i.e., field surveys). Hence the need for a fast, objective and quantitative classification of explosive activity. Here, we use images of the monitoring camera network, seismicity and ground deformation data, to characterize and distinguish paroxysms, impacting the whole island, from major explosions, that affect the summit of the volcano above 500 m elevation, and from the persistent, mild explosive activity that normally has no impact on the local population. This analysis comprises 12 explosive events occurring at Stromboli after 25 June 2019 and is updated to 6 December 2020

Study of Surface Emissions of 220Rn (Thoron) at Two Sites in the Campi Flegrei Caldera (Italy) during Volcanic Unrest in the Period 2011–2017

The study concerns the analysis of 220Rn (thoron) recorded in the surface soil in two sites of the Campi Flegrei caldera (Naples, Southern Italy) characterized by phases of volcanic unrest in the seven-year period 1 July 2011–31 December 2017. Thoron comes only from the most surface layer, so the characteristics of its time series are strictly connected to the shallow phenomena, which can also act at a distance from the measuring point in these particular areas. Since we measured 220Rn in parallel with 222Rn (radon), we found that by using the same analysis applied to radon, we obtained interesting information. While knowing the limits of this radioisotope well, we highlight only the particular characteristics of the emissions of thoron in the surface soil. Here, we show that it also shows some clear features found in the radon signal, such as anomalies and signal trends. Consequently, we provide good evidence that, in spite of the very short life of 220Rn compared to 222Rn, both are related to the carrier effect of CO2, which has significantly increased in the last few years within the caldera. The hydrothermal alterations, induced by the increase in temperature and pressure of the caldera system, occur in the surface soils and significantly influence thoron's power of exhalation from the surface layer. The effects on the surface thoron are reflected in both sites, but with less intensity, the same behavior of 222Rn following the increasing movements and fluctuations of the geophysical and geochemical parameters (CO2 flux, fumarolic tremor, background seismicity, soil deformation). An overall linear correlation was found between the 222−220Rn signals, indicating the effect of the CO2 vector. The overall results represent a significant step forward in the use and interpretation of the thoron signal