ALBERT

Description: Over fifty eruptive episodes with Strombolian activity, lava fountains, and lava flows occurred at Mt Etna volcano between 2006 and 2013. Namely, there were seven paroxysmal lava fountains at the South-East Crater in 2007-2008 and 46 at the New South-East Crater between 2011 and 2013. Lava emissions lasting months affected the upper eastern flank of the volcano in 2006 and 2008-2009. Effective monitoring and forecast of such volcanic phenomena are particularly relevant for their potential socio-economic impact in densely populated regions like Catania and its surroundings. For example, explosive activity has often formed thick ash clouds with widespread tephra fall able to disrupt the air traffic, as well as to cause severe problems at infrastructures, such as highways and roads. Timely information about changes in the state of the volcano and possible onset of dangerous eruptive phenomena requires efficacious surveillance methods. The analysis of the continuous background seismic signal, the so-called volcanic tremor, turned out of paramount importance to follow the evolution of volcanic activity [e.g., Alparone et al., 2003; Falsaperla et al., 2005]. Changes in the state of the volcano as well as in its eruptive style are usually concurrent with variations of the spectral characteristics (amplitude and frequency) of tremor. The huge amount of digital data continuously acquired by INGV’s broadband seismic stations every day makes a manual analysis difficult. In order to tackle this problem, techniques of automatic classification of the tremor signal are applied. In a comparative study, the robustness of different methods for the identification of regimes in volcanic activity were examined [Langer et al., 2009]. In particular, Langer et al. [2011] applied unsupervised classification techniques to the tremor data recorded at one station during seven paroxysmal episodes in 2007-2008. Their results revealed significant changes in the pattern classification well before the onset of the eruptive episodes. This evidence led to the development of specific software packages, such as the program KKAnalysis [Messina and Langer, 2011], a software that combines an unsupervised classification method (Kohonen Maps) with fuzzy cluster analysis. The operational characteristics of these tools - fail-safe, robustness with respect to noise and data outages, as well as computational efficiency - allowed on-line processing at the operative centre of the INGV-Osservatorio Etneo in 2010 and the identification of criteria for automatic alarm flagging. The system is hitherto one of the main automatic alerting tools to identify impending eruptive events at Etna. The software carries out the on-line processing of the new data stream coming from two seismic stations, merged with reference datasets of past eruptive episodes. In doing so, results obtained for new data are immediately compared to previous eruptive scenarios. Given the rich material collected in recent years, we are able to apply the alert system to eleven stations at different elevations (1200-3050 m) and distances (1-8 km) from the summit craters. Critical alert parameters were empirically defined to obtain an optimal tuning of the alert system for each station. To verify the robustness of this new, multistation alert system, a dataset encompassing about eight years of continuous seismic records (since 2006) was processed automatically using KKAnalysis and collateral software off-line. Then, we analyzed the performance of the classifier in terms of timing and spatial distribution of the stations. We also investigated the performance of the new alert system based on KKAnalysis in case of activation of whatever eruptive centre. Intriguing results were obtained in 2010 throughout periods characterized by the renewal of volcanic activity at Bocca Nuova-Voragine and North-East Crater, and in the absence of paroxysmal phenomena at South-East Crater and New South-East Crater. Despite the low-energy phenomena reported by volcanologists (i.e., degassing, low-to moderate explosions), the triggered alarms demonstrate the robustness of the classifier and its potential: i) to identify even subtle changes within the volcanic system using tremor, and ii) to highlight the activation of a single eruptive centre, even though different from the one for which the classifier was initially tested. It is worth noting that in case of activation of weak sources, the successful performance of the classifier depends upon the general level of signals originating from other sources in that specific time span.

Description: Published

Description: Nicolosi (Catania, Italy)

Description: 2V. Dinamiche di unrest e scenari pre-eruttivi

Description: open

Description: Mt. Etna is permanently active requiring a continuous data acquisition a multidisciplinary monitoring system where huge data masses accumulate and pose severe difficulties of interpretation. Therefore the INGV staff has developed a number of software tools for data mining, aiming at identifying structures in the data which can be related to the volcanic activity and furnish criteria for the definition of alert systems. We tackle the problem by applying methods of clustering and classification. We identify data groups by defining a measure of similarity or distance. Data groups may assume various shapes, once forming convex clouds once complex concave bodies. The tool “KKAanalysis” is a basket of clustering methods and forms the backbone of the tremor-based automatic alarm system of INGV-OE. It exploits both SOM and Fuzzy Clustering. Besides seismic data the concept has been applied to petrochemic data as well as in a combined analysis of gas-emission data and seismic data. The software “DBSCAN” focuses on density-based clustering that allows discovering clusters with arbitrary shape. Here, clusters are defined as dense regions of objects in the data space separated by regions of low density. In DBSCAN a cluster grows guaranteeing that the density within a group of objects exceeds some threshold. In the context of volcano monitoring the method is particularly promising in the recognition of ash particles as they have a rather irregular shape. The “MOTIF” software allows identifying typical wave forms in time series. It overcomes shortages of methods like cross- correlation, which entail a high computational effort. MOTIF on the other hand can recognize non-similarity of two patterns on a small number of data points without going through the whole length of the data vectors. The development includes modules for feature extraction and post-processing verifying the validity of the results obtained by the classifiers.

Description: Published

Description: Nicolosi (Catania, Italy)

Description: 2V. Dinamiche di unrest e scenari pre-eruttivi

Description: open

Description: Timely identification of changes in the state of volcanoes and onset of potentially dangerous eruptive phenomena requires efficacious surveillance methods. In the case of an active volcano like Mt Etna, the continuous background seismic signal called volcanic tremor is of paramount importance. The huge amount of continuously acquired digital data entails the necessity of data reduction and parameter extraction. For this purpose, techniques of automatic analysis of volcanic tremor were applied by INGV for the real time monitoring of this signal. We checked the possibility to identify regimes of volcanic activity based on pattern classification of volcanic tremor. A specific software named “KKAnalysis” was developed. It combines various unsupervised classification methods (Kohonen Maps and fuzzy cluster analysis) and forms the backbone of an automatic alert system at INGV-OE. Besides its near real time application, it can be operated off-line, allowing an efficient a-posteriori processing of data and tuning of the alarm criteria to match specific needs of sensitivity and robustness. An ongoing development of this tool will allow us to include a large number of seismic stations in a multistation-alarm system. The new system will be more robust in case of failure of single sensors, and will achieve a better coverage of the various eruptive craters. In an off-line test, we exploited a dataset covering eight years of seismic records, and analysed the performance of the new system in terms of “trigger timing” and spatial distribution of the stations. Intriguing results were obtained throughout periods of renewal of volcanic activity at Bocca Nuova-Voragine and North East Crater, and in the absence of paroxysmal phenomena at South East Crater and New South East Crater.

Description: Published

Description: Nicolosi (Catania, Italy)

Description: 2V. Dinamiche di unrest e scenari pre-eruttivi

Description: open