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In this work, an Active Learning approach for improving the classification of passed seismo-volcanic events is proposed. Here we study the specific case of Explosion Quakes from Stromboli Volcano versus other seismo-volcanic events, recorded as seismograms, and the use of Random Forest as a Classification method. In conformity with the active learning paradigm, the approach recalls the human intervention for the annotation of uncertain data. The uncertainty is established by the event probabilities, predicted by a trained random forest classifier. The human intervention consists of editing and relabelling the data into these main three classes: Explosion Quakes, Non-Explosion Quakes or Non-Classifiable. The edited events are added as new training examples with the purpose of improving future predictions. The results demonstrate that the proposed active learning approach improves the probability distribution of the events after the human intervention, in particular, after a cut-out phase of the signals with low probabilities.

The elastic energy released through Strombolian explosions has been quite studied, detailing various processes, sources, and precursory events at several volcanoes. We realized an analysis based on the relative partitioning of the elastic energy radiated into the atmosphere and ground by Strombolian-type explosions recorded at El Reventador volcano, using infrasound and seismic signals at high and moderate seismicity episodes during intense eruptive stages of explosive and effusive activity. Our results show that considerable values of Volcano Acoustic-Seismic Ratio (VASR or η) are obtained at high seismicity stages. VASR is a physical diagnostic of explosive degassing that we used to compare eruption mechanisms at El Reventador volcano for two datasets of explosions recorded at a Broad-Band BB seismic and infrasonic station located at ~5 kilometers from the vent. We conclude that the acoustic energy EA released during explosive activity (VASR η = 0.47, standard deviation σ = 0.8) is higher than the EA released during effusive activity; therefore, producing the highest values of η. Furthermore, we realized the analysis and characterization of the eruptive intensity for two episodes at high seismicity, calculating a η three-time higher for an episode of effusive activity with an occasional explosive component (η = 0.32, and σ = 0.42), than a η for an episode of only effusive activity (η = 0.11, and σ = 0.18), but more energetic., {"references":["Baby, P., Rivadeneira, M., & Barragán, R. (2014). La Cuenca Oriente: Geología Y Petróleo. Versión Actualizada. Quito: Ifea, Ird, Petroamazonas Ep. 3ra Ed. Edición Especial Conmemorativa Por Los 80 Años De Schlumberger En El Ecuador.","Battaglia, J., Métaxian, J.-P., & Garaebiti, E. (2016). Short term precursors of Strombolian explosions at Yasur volcano (Vanuatu). Geophysical Research Letters, No 43, doi: 10.1002/2016GL067823., 1-6.","Bourquin, J., Samaniego, P., Ramón, P., Bonadonna, C., Kelfoun, K., Vallejo, S., Mothes, P. (2011). Mapas De Los Peligros Potenciales Del Volcán Reventador. Quito: IG-EPN.","Chouet, B., Dawson, P., & Martini, M. (2008). Shallow-Conduit Dynamics at Stromboli Volcano, Italy, Imaged From Waveform Inversions. Geological Society, London, Special Publications, 307 doi:10.1144/SP307.5, 57-84.","Chouet, B., Saccorotti, G., Martini, M., Dawson, P., De Luca, G., Milana, G., & Scarpa, R. (1997). Source and path effects in the wave fields of tremor and explosions at Stromboli Volcano, Italy. JOURNAL OF Geophysical Research, Vol. 102, 15129-15150.","GVP. (2014). Reventador (Ecuador) Ongoing ash emissions, lava flows, and pyroclastic flows through July 2014. Global Volcanism Program. Report on Reventador (Ecuador). In: GVP Staff (ed.), Bulletin of the Global Volcanism Network, 39:7. Smithsonian Institution. Retrieved 4 March 2017 from: http://dx.doi.org/10.5479/si.GVP.BGVN201407-352010.","Hall, M. (1977). El Volcanismo en el Ecuador. Quito: Inst. Pan. Geog. Hist.","Hall, M. (1980). El Reventador, Ecuador, Un Volcán Activo De Los Andes Septentrionales. Politécnica, Monografía De Geología. Vol. V, No 2, 123-136.","IG-EPN. (2015). Informe Especial del Volcán Reventador N°1, 2015. Evaluación de la actividad eruptiva del volcán Reventador. Quito: Escuela Politecnica Nacional. Instituto Geofisico. Retrieved 5 March 2017 from: http://igepn.edu.ec/servicios/noticias/959-informe-especial-del-volcan-reventador-n-1.\n[10]\tIG-EPN. (2015). Informe Especial del Volcán Reventador N°2, 2015. Nuevo flujo de lava en el volcán Reventador. Quito: Escuela Politecnica Nacional. Instituto Geofisico. Retrieved 5 March 2017 from: http://www.igepn.edu.ec/reventador-informes/rev-especiales/rev-e-2015/12900-infespreventador2-19mayo2015/file.\n[11]\tIG-EPN. (2015). Informe Especial del Volcán Reventador N°3, 2015. 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We examined one year of seismic recordings collected in 2008 during a temporary experiment at Yasur volcano (Vanuatu). The volcano has a permanent Strombolian activity that was at a relatively high level during most of our experiment with commonly at least one explosion per minute. Associated with this activity, the network recorded intense seismicicity with hundreds of transients per day. Video recordings indicate that most of the high frequency transients are directly related to the strombolian explosions. They also outline the presence of fewer signals which are not accompanied by any surface activity. The classification of transient events recorded at a station close to the summit indicates that a significant part of the events exhibit waveform similarity. This technique allows the identification of characteristic repeating events among the hundreds of thousands of transients recorded during the experiment. Most of the families of similar events are groups of explosion quakes (EQs) but a few are groups of Long Period events related to deeper processes. By scanning the 9 months of continuous data available at the summit station with master events extracted from these families we reconstruct their temporal evolution. Our results show that several families dominate the activity with a few of them lasting for several months. We show that their temporal evolutions can be used to probe changes in the structure or activity of the volcano. We observe that a major change was induced by a M = 7.3 subduction earthquake which occurred on April 9, 2008 about 80 km from the volcano. While this event did not change significantly the surface morphology of the volcano nor the intensity of the eruptive activity, it interrupted the families as none of them is present both before and after the event. This change in the waveforms can be explained by a drop in the seismic velocity of the volcano caused by the distal event. Numerous other transitions between families are observed, sudden or progressive. These can be interpreted as representative of changes in the eruptive dynamics. The presence of similar EQs, especially for impulsive explosions, indicates that the source mechanism is reproducible and has a stable location for some periods. This favors a source process based on the oscillation of the conduit or oscillation of the edifice in response to the explosive decompression of gas slugs at the free surface of the conduit. Our results suggest that the seismic activity of Yasur is characterized by the presence of dominant modes of resonance of the conduit which may be influenced both by external and internal factors.

Independent component analysis (ICA), applied on low-frequency (LF) events recorded at Stromboli volcano, reveals that these signals are composed of three independent components in three different frequency bands (approximately 0.8–1.2, 2.4–3.0, 3.2–4.5 Hz, respectively). The first two frequency ranges are mainly composed of body waves coming from a direction in a range of [−30°, 30°] around the crater area. This result is a clear indication that the entire signal, in these frequency bands, comes from the source area. In fact, any scattered wave should have different back azimuth propagation, due to the random distribution of scatterers' location. However, the last independent component is dominated by other kinds of waves, coming from many different directions.

The application of neural networks as classifiers of seismic events is described with the aim of developing an automatic system for the classification of ‘explosion quakes’ at the Stromboli volcano. The architecture of the network that we trained to identify four different classes of shocks was a Multi-Layer Perceptron, using the Back Error Propagation algorithm. Five different approaches for representing the information embedded in the seismograms, both in the time and in the frequency domain, were considered, and the results compared. The direct use of the time series of the shocks was not satisfactory. The auto-correlation function worked well, but in some cases it was misleading. A better performance was obtained with a frequency domain representation. Finally, the use of the envelope function did not work well. Combining parameters such as the auto-correlation and envelope functions can improve one source of error, but it may introduce new ones. The performance obtained highlights the importance of the data attributes used for the training of the network. Topologies with eight neurons in a single hidden layer gave, on average, the best results among the considered neural network structures. The overall results provide a large number of events (89% with the best performance) correctly classified, indicating that this automatic technique is reliable, and encouraging further applications in the field of volcanic seismology.

We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (