Rockfall Episodes from Visual and Seismic Data Analysis at Stromboli Volcano, Italy

On 30 December, 2002, huge subaerial and submarine landslides (Bonaccorso et al., 2003) occurred at Stromboli volcano, Italy, two days after a renewal of the effusive activity. As a consequence of the landslides and concurrent tsunami waves, which threatened the safety of the inhabitants of the island (Pino et al., 2004), the attention of the scientific community has been drawn on sliding processes affecting the instable flanks of the Sciara del Fuoco in the western part of the volcano (e.g., Maiolino et al., 2004). We analyze rockfall episodes which have been continuing to occur despite the end of the lava effusion in July 2003. Particularly, we propose a comparative analysis of visual and seismic data recorded in 2004. Our data set encompasses records of the seismic network, along with concurrent visual images of permanent video cameras - in continuous acquisition - run by INGV, pointing from a site at 400 m above see level to the summit part of the volcano. Excluding night-time hours and days with bad weather conditions and/or when the vapor emission hindered the view, we find that only a few seismic traces refer to rockfall episodes which are not visible on the field. This finding allows us to explore the characteristics of the rockfalls in a new perspective, integrating visual and seismic data. Additionally, this comparative analysis sheds light on the sliding process, considering the material involved and possible cause-and-effect relationships with seismic-shaking and eruptive activity

Activity Regimes on Mt Etna inferred from Automatic Unsupervised

Mt Etna is among the best monitored basaltic volcano worldwide. High-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Seismic data sets cover decades long local recordings, obtained during different regimes of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Earthquakes swarms have often heralded effusive activity. However, volcanic tremor – the persistently radiated signal by the volcano - has proved to be a key indicator of impending eruptive activity. Changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. We apply a recently developed software for the analysis of volcanic tremor, combining Kohonen Maps along with Cluster and Fuzzy Analysis, in order to identify transitions from pre-eruptive to eruptive activity. Throughout the analysis of the data flow, the software provides an unsupervised classification of the spectral characteristics (i.e., amplitude and frequency content) of the signal, which is interpreted in the context of a specific state of the volcano. We present an application on the eruptive events occurred during the 2007-2009 time period, encompassing 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, and a lava emission on the upper east flank of the volcano, which started on 13 May 2008 and ended on 6 July 2009. In this time span the source of volcanic tremor was always shallow (less than 3 km), i. e., within the volcano edifice. From the analysis we conclude that the upraise of magma to the surface was fast, taking several hours to a few minutes. We discuss the possible reasons of such variability in the light of the characteristics of the overall seismicity preceding the eruptions in the study period, taking into account field observations and rheology of the ascending magma as well

UAV Thermal Infrared Remote Sensing of an Italian Mud Volcano

Extreme environments like active volcanoes exhibit many difficulties in being studied by in situ techniques. For exam-ple, during eruptions, summit areas are very hard to be accessed because of logistics problems and/or volcanic hazards. The use of remote sensing techniques in the last 20 years by satellite or airborne platforms has proven their capabilities in mapping and monitoring the evolution of volcanic activity. This approach has become increasingly important, as much interest is actually focused on understanding precursory signals to volcanic eruptions. In this work we verify the use of cutting-edge technology like unmanned flying system thermally equipped for volcanic applications. We present the results of a flight test performed by INGV in collaboration with the University of Bologna (Aerospace Division) by using a multi-rotor aircraft in a hexacopter configuration. The experiment was realized in radio controlled mode to overcome many regulation problems which, especially in Italy, limit the use of this system in autonomous mode. The overall goal was not only qualitative but also quantitative oriented. The system flew above an Italian mud volcano, named Le Salinelle, located on the lower South West flank of Mt. Etna volcano, which was chosen as representative site, providing not only a discrimination between hot and cold areas, but also the corresponding temperature values. The in-flight measurements have been cross-validated with contemporaneous in-situ acquisition of thermal data and from independent measurements of mud/water temperature

Pattern classification of volcanic tremor data related to the 2007-2012 Mt. Etna (Italy) eruptive episodes

From March 2007 to April 2012 one of the main craters of Mt. Etna volcano, the South East Crater, was frequently active with spectacular, even though low dangerous, eruptions mainly in form of lava fountains. Thirty-three eruptive episodes occurred at that crater, encompassing thirty-two paroxysmal lava fountains (seven in 2007-2008 and twenty-five in 2011-2012), and a lava emission, started on 13 May 2008 and ended on 6 July 2009, along the upper eastern flank of the volcano. From the seismic point of view, the onset of all these eruptions was heralded by changes in the spectral characteristics of volcanic tremor recorded by digital broadband stations, which permanently monitor the volcanic region. On the basis of the tremor data collected between 2007 and 2009, some of us (Messina and Langer) developed a software which, combining unsupervised classification methods based on Kohonen Maps and the fuzzy cluster analysis, allows to identify transitions from pre-eruptive to eruptive activity through the classification of the tremor characteristics (i.e. amplitude and frequency content). Since 2010 an on-line version of this software is adopted at the Osservatorio Etneo as one of the automatic alerting tools to identify early stages of eruptive events. The software carries out the analysis of the continuous data stream of two key seismic stations, for which reference datasets were elaborated taking into account the tremor data recorded during the eruptive episodes from 2007 to 2009. The numerous paroxysmal eruptions occurred in 2011-2012 and the improved network density, in particular on the summit crater area, after 2009, lead us to extend the application of automatic volcanic tremor classification by using a larger number of stations at different elevation and distance from the summit craters. Datasets have been formed for the new stations, while for the previous key stations, the reference datasets were updated adding new patterns of the tremor signal. We discuss the performances of the classifier for the various stations in terms of timing of the early variations and spatial distribution of the stations

Application of a multi­station alert method for short­-term forecasting of eruptions at Etna, Italy

From 11 January to 15 November 2011, 18 paroxysmal eruptions occurred at Etna, Italy. These events belong to a long sequence of eruptive episodes, which marked the prevalent explosive style of the volcano since the early 2000s. Applying “KKAnalysis”, a software for pattern classification that combines Self­Organizing Maps and fuzzy clustering, to the background seismic radiation (so-called volcanic tremor), we were able to detect critical changes in the spectral characteristics (amplitude and frequency content) at a very early stage of the volcano unrest. The online implementation for surveillance purposes of KKAnalysis provided automatic alert of the impending eruptive events from hours to a few days in advance. In its original version, the classifier analyzed the data stream continuously recorded at a single seismic station. By using offline a modified version of KKAnalysis, here we apply the software to the seismic signal recorded at 11 broadband stations in 2011. The seismic sensors were located at various distances (from 1 to 8 km) from the active craters. The continuous records and the optimal geometry of the seismic network offer us the possibility to track the spectral variations in time and space. We show the new results of pattern classification and propose a revised, more powerful multi­station alert method that now provides short­ term forecasting also in the form of animated maps that flag the detection of changes at each station. This allows us to observe how the unrest develops in various sectors of the volcano. We discuss the performance of the method and the robustness of the eruption forecasts in the context of the complex dynamics of a volcanic system such as Etna

The endemic vascular flora of Peloritani Mountains (NE Sicily): Plant functional traits and phytogeographical relationships in the most isolated and fragmentary micro-plate of the Alpine orogeny

This study is aimed at (1) producing a complete and updated inventory of the endemic vascular flora of Peloritani Mountains, (2) defining the geographical limits of Peloritani, regarded here as a biogeographical district and (3) highlighting possible paleogeographic connections with other Mediterranean lands. The heterogeneity analysis of the endemic flora was performed by means of contingency tables, through the x 2 test. The endemic flora of this area consists of 129 specific and infraspecific taxa, of which 15 are restricted to the Peloritani Mountains. The analysis of habitats revealed that endemic taxa are most abundant on cliffs, rangelands, woods and garrigues. A large number of surveyed taxa are endemic to central-southern Italy and Sicily, while the number of endemic taxa in common with Calabria, Etna and Aeolian Islands turned out to be rather low, in spite of the geographical proximity. The endemic flora of Peloritani allows to emphasize palaeogeographical relationships not only with the neighbouring Mediterranean territories, but also with currently remote ones, such as southern Spain, Sardinia and Corsica. The phytogeographical framework substantiates the hypothesis that the Peloritani floristic district coincides with the limit given to Peloritani Mountains by structural geologists

Quantitative Systems Pharmacology and Biased Agonism at Opioid Receptors: A Potential Avenue for Improved Analgesics

Chronic pain is debilitating and represents a significant burden in terms of personal and socio-economic costs. Although opioid analgesics are widely used in chronic pain treatment, many patients report inadequate pain relief or relevant adverse effects, highlighting the need to develop analgesics with improved efficacy/safety. Multiple evidence suggests that G protein-dependent signaling triggers opioid-induced antinociception, whereas arrestin-mediated pathways are credited with modulating different opioid adverse effects, thus spurring extensive research for G protein-biased opioid agonists as analgesic candidates with improved pharmacology. Despite the increasing expectations of functional selectivity, translating G protein-biased opioid agonists into improved therapeutics is far from being fully achieved, due to the complex, multidimensional pharmacology of opioid receptors. The multifaceted network of signaling events and molecular processes underlying therapeutic and adverse effects induced by opioids is more complex than the mere dichotomy between G protein and arrestin and requires more comprehensive, integrated, network-centric approaches to be fully dissected. Quantitative Systems Pharmacology (QSP) models employing multidimensional assays associated with computational tools able to analyze large datasets may provide an intriguing approach to go beyond the greater complexity of opioid receptor pharmacology and the current limitations entailing the development of biased opioid agonists as improved analgesics