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

New inferences from spectral seismic energy measurement of a link between regional seismicity and volcanic activity at Mt. Etna, Italy

The existence of a relationship between regional seismicity and changes in volcanic activity has been the subject of several studies in the last years. Generally, activity in basaltic volcanoes such as Villarica (Chile) and Tungurahua (Ecuador) shows very little changes after the occurrence of regional earthquakes. In a few cases volcanic activity has changed before the occurrence of regional earthquakes, such as observed at Teide, Tenerife, in 2004 and 2005 (Tárraga et al., 2006). In this paper we explore the possible link between regional seismicity and changes in volcanic activity at Mt. Etna in 2006 and 2007. On 24 November, 2006 at 4:37:40 GMT an earthquake of magnitude 4.7 stroke the eastern coast of Sicily. The epicenter was localized 50 km SE of the south coast of the island, and at about 160 km from the summit craters of Mt. Etna. The SSEM (Spectral Seismic Energy Measurement) of the seismic signal at stations at 1 km and 6 km from the craters highlights that four hours before this earthquake the energy associated with volcanic tremor increased, reached a maximum, and finally became steady when the earthquake occurred. Conversely, neither before nor after the earthquake, the SSEM of stations located between 80 km and 120 km from the epicentre and outside the volcano edifice showed changes. On 5 September, 2007 at 21:24:13 GMT an earthquake of magnitude 3.2 and 7.9 km depth stroke the Lipari Island, at the north of Sicily. About 38 hours before the earthquake occurrence, there was an episode of lava fountain lasting 20 hours at Etna volcano. The SSEM of the seismic signal recorded during the lava fountain at a station located at 6 km from the craters highlights changes heralding this earthquake ten hours before its occurrence using the FFM method (e.g., Voight, 1988; Ortiz et al., 2003). A change in volcanic activity – with the onset of ash emission and Strombolian explosions – was observed a couple of hours before the occurrence of the regional earthquakes. It can be interpreted as the magmatic response to a change of the distribution of tectonic stress in the edifice before the earthquake. In the light of this hypothesis, we surmise that the magmatic system behaved similar to a dilatometer and promise news lines to forecasting the volcano 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

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

Delayed neonatal visual evoked potentials are associated to asymmetric growth pattern in twins

Objectives: To study the association between intrauterine growth and visual pathways maturation by neonatal visual evoked potentials (VEPs) in twins, in view of a possible prognostic role. Methods: Seventy-four twin neonates from 37 pregnancies were selected based on gestational age of more than 30 weeks and uneventful perinatal clinical course. Flash VEPs were recorded at the same postmenstrual age in each twin pair. The association between P2 latency and anthropometric variables at birth was analyzed by comparison within each twin pair and regarding each variable as ordered difference between the two twins. Results: Analysis of differences within each twin pair highlighted that inter-twin difference in P2 latency was significantly related to difference in ponderal index (PI) (p = 0.048). Expressing the difference in latency as a categorical binary variable, the correlation was significant for both difference in PI, (median difference = −0.36, 95% CI −0.54 to −0.14, p = 0.001) and difference in body mass index (BMI), (median difference = −1.06, 95% CI −1.74 to −0.29, p = 0.006). Conclusions: Lower values of PI and BMI differences are associated to delayed VEP latency in twin pairs. Significance: VEP latency suggests reduced myelination of visual pathways when difference in growth pattern occurs in twins

A Software Package for Unsupervised Pattern Recognition and Synoptic Representation of Results: Application to Volcanic Tremor Data of Mt Etna

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form

Identification of activity regimes by unsupervised pattern classification of volcanic tremor data. Case studies from Mt. Etna

The monitoring of the seismic background signal – commonly referred to as volcanic tremor - has become a key tool for volcanic surveillance, particularly when field surveys are unsafe and/or visual observations are hampered by bad weather conditions. Indeed, it could be demonstrated that changes in the state of activity of the volcano show up in the volcanic tremor signature, such as amplitude and frequency content. Hence, the analysis of the characteristics of volcanic tremor leads us to pass from a mere monoparametric vision of the data to a multivariate one, which can be tackled with modern concepts of multivariate statistics. For this aim we present a recently developed software package which combines various concepts of unsupervised classification, in particular cluster analysis and Kohonen maps. Unsupervised classification is based on a suitable definition of similarity between patterns rather than on a-priori knowledge of their class membership. It aims at the identification of heterogeneities within a multivariate data set, thus permitting to focalize critical periods where significant changes in signal characteristics are encountered. The application of the software is demonstrated on sample sets derived from Mt. Etna during eruptions in 2001, 2006 and 2007-8

Regimes of Volcanic Activity at Mt. Etna in 2007-2009 inferred from Unsupervised Pattern Recognition on Volcanic Tremor Data

Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Particularly, volcano-seismic data sets cover decades long local recordings, temporally encompassing different styles of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Intense earthquakes swarms have often heralded effusive activity. However, from the seismic point of view, volcanic tremor has proved to be one of the most reliable indicators of impending eruptive activity. Indeed, changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. Some of us (Langer and Messina) have recently developed a new software for the classification of volcanic tremor data, combining Self Organizing Maps (also known as Kohonen Maps) along with Cluster and Fuzzy Analysis. This software allows us to analyse the background seismic radiation at permanent broadband stations located at various distance from the summit craters 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. The information embedded in the spectrum is interpreted to assign a specific state of the volcano. An application of this new software is proposed here on the eruptive events at Etna of 2007-2009, which consisted of 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, followed by lava emissions on the upper east flank of the volcano, with start on 13 May 2008 and end on 6 July 2009. In the study period the source of volcanic tremor was always shallow (less than 3 km) and within the volcano edifice. The upraise of magma to the surface was fast and associated with changes of volcanic tremor features, which covered time windows of variable duration from 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

Congenital generalized hypertrichosis: The skin as a clue to complex malformation syndromes

Hypertrichosis is defined as an excessive growth in body hair beyond the normal variation compared with individuals of the same age, race and sex and affecting areas not predominantly androgen-dependent. The term hirsutism is usually referred to patients, mainly women, who show excessive hair growth with male pattern distribution. Hypertrichosis is classified according to age of onset (congenital or acquired), extent of distribution (generalized or circumscribed), site involved, and to whether the disorder is isolated or associated with other anomalies. Congenital hypertrichosis is rare and may be an isolated condition of the skin or a component feature of other disorders. Acquired hypertrichosis is more frequent and is secondary to a variety of causes including drug side effects, metabolic and endocrine disorders, cutaneous auto-inflammatory or infectious diseases, malnutrition and anorexia nervosa, and ovarian and adrenal neoplasms. In most cases, hypertrichosis is not an isolated symptom but is associated with other clinical signs including intellective delay, epilepsy or complex body malformations. A review of congenital generalized hypertrichosis is reported with particular attention given to the disorders where excessive diffuse body hair is a sign indicating the presence of complex malformation syndromes. The clinical course of a patient, previously described, with a 20-year follow-up is reported

Early onset retinal dystrophies: Clinical clues to diagnosis for pediatricians

Introduction: Inherited retinal dystrophies are major cause of severe progressive vision loss in children. Early recognition and diagnosis are essential for timely visual rehabilitation during the appropriate stages of the visual development, as well as for genetic diagnosis and possible gene therapy. The aim of this study is to characterize a pattern of the initial visual symptoms, which could help the pediatricians and the primary care providers to suspect an inherited retinal disorder in its early stage. Methods: We analyzed the initial clinical symptoms, based on parental report during the first visit to specialist, in 50 children diagnosed with retinal dystrophy confirmed by full-field electroretinography. The analysis included the age of symptoms onset and the type of visual symptoms, both in the total population and in the following diagnostic subgroups: rod-cone dystrophy (n.17), cone-rod dystrophy (n.12), achromatopsia (n.13), congenital stationary night blindness (n.6) and Leber's congenital amaurosis (n.2). Results: The majority of children (80%) had the onset of clinical symptoms before one year of age. The most frequent visual complaints reported by parents were nystagmus (76%), visual loss (28%) and photophobia (8%). Nystagmus was the first symptom reported by parents if the disease onset was before the age of six months, while the onset after the six months of age was more likely associated with the complain of vision loss. Conclusions: Low vision and nystagmus observed by parents, particularly in the first year of life, may represent a red flag, prompting an appropriate ophthalmological workup for inherited retinal dystrophy