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This paper proposes a computer-based classifier to automatically identify four seismic event classes of the Llaima volcano, one of the most active volcanoes in the Southern Andes, situated in the Araucanía Region of Chile. A combination of features that provided good recognition performance in our previous papers concerning the Llaima and Villarica (located 100 km south of Llaima) volcanoes is utilized in order to train the classifiers. These features are extracted from the amplitude, frequency and phase of the seismic signals. Unlike the previous works where fixed length windows were used to obtain the seismic signals, this paper employs signals of variable lengths that span the entire seismic event. The classifiers are implemented using support vector machines. A confidence analysis is also included to improve reliability of the classification. Results indicate that the features used for recognition of the events of Villarica volcano also provide good recognition results for the Llaima volcano, yielding classification exactitude of over 80%. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges and opportunities for cross-border eruption management between Chile and Argentina, focussing on an examination of the 2011–2012 eruption of Cordon Caulle in Chile and other crises between 2012 and 2015. We discuss the differences between Chile and Argentina in volcano monitoring, eruption impacts, eruption management and governance during this timeframe. We also discuss the issues in communication of the risks and the potential for future integration at the scientific level. The study is based on 31 interviews with scientists and local officials in 2014–15 (15 in Chile and 16 in Argentina), a questionnaire survey of 128 Argentinian residents in 2015 and a questionnaire survey of 25 disaster managers and residents of Pucón during the 2015 eruption of Villarrica volcano. We conclude that in 2011–12, there were issues with communication across the border, particularly at the political level, and that there are important ways that the scientific institutions in both countries complement each other. • We present new social data concerning the 2011–12 eruption of Puyehue Cordon Caulle. • We show that the eruption was significant in the evolution of institutions and agreements between the countries. • We show that locally, there was dissatisfaction with risk communication in Argentina (which was worse affected). • We argue that the scientific institutions complement each other well but need more resourcing from government. [ABSTRACT FROM AUTHOR]

Automatic pattern recognition applied to seismic signals from volcanoes may assist seismic monitoring by reducing the workload of analysts, allowing them to focus on more challenging activities, such as producing reports, implementing models, and understanding volcanic behaviour. In a previous work, we proposed a structure for automatic classification of seismic events in Llaima volcano, one of the most active volcanoes in the Southern Andes, located in the Araucanía Region of Chile. A database of events taken from three monitoring stations on the volcano was used to create a classification structure, independent of which station provided the signal. The database included three types of volcanic events: tremor, long period, and volcano–tectonic and a contrast group which contains other types of seismic signals. In the present work, we maintain the same classification scheme, but we consider separately the stations information in order to assess whether the complementary information provided by different stations improves the performance of the classifier in recognising seismic patterns. This paper proposes two strategies for combining the information from the stations: i) combining the features extracted from the signals from each station and ii) combining the classifiers of each station. In the first case, the features extracted from the signals from each station are combined forming the input for a single classification structure. In the second, a decision stage combines the results of the classifiers for each station to give a unique output. The results confirm that the station-dependent strategies that combine the features and the classifiers from several stations improves the classification performance, and that the combination of the features provides the best performance. The results show an average improvement of 9% in the classification accuracy when compared with the station-independent method. [ABSTRACT FROM AUTHOR]