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Brancato, Alfonso
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Preferred name
Brancato, Alfonso
Email
alfonso.brancato@ingv.it
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staff
ORCID
Researcher ID
B-8198-2014
16 results
Now showing 1 - 10 of 16
- PublicationRestrictedProbability hazard map for future vent opening at Etna volcano (Sicily, Italy).(2014-10-29)
; ; ; ; ; ; ;Alfonso, Brancato; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Mauro, Coltelli; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Placido, Montalto; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Domenico, Patanè; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cristina, Proietti; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Danila, Scandura; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; ; Mount Etna is a composite stratovolcano located along the Ionian coast of eastern Sicily. The frequent occurrence of flank eruptions (at an interval of years), mostly concentrated along the NE, S and W rift zones leads to a high volcanic hazard that, linked with intense urbanization, poses a high volcanic risk. In the framework of the project PON SIGMA (Integrated Cloud-Sensor System for Advanced Multirisk Management), we develop a near real-time computer-assisted analysis and probabilistic evaluations that provides the identification of the areas prone to the highest vent opening hazard. A longterm volcanic hazard assessment, mainly based on the past flank activity of the Mt. Etna volcano, is the basic tool for the evaluation of this risk. Then, a reliable forecast of where an impending eruption will occur is needed. The use of a code such BET_EF (Bayesian Event Tree_Eruption Forecasting) delivers a long-term hazard map, that, if additional data are provided, switches into a short-term future vent opening map. The present application is based on incoming seismic and ground deformation data. Analytic inversion of high frequencies deformation data is performed to find the key parameters of a magmatic source in an elastic, isotropic and homogeneous half-space. Seismic data allow us to set the boundary of the investigated area. The inversion is performed by using the genetic algorithms (GAs) approach, a well-known search technique widely used to solve optimization problems and categorized as global search heuristics (Goldberg, 1989). Hence the magmatic source is located, a forward model is computed to evaluate the deformation field over Mt. Etna surface. Therefore, for each cell, the displacement vector modulus is estimated and the density probability function is calculated. A higher probability value matches with the cells with larger modulus, whereas lower estimate is found where the modulus is close to zero, being the sum of the probability values normalized to one over the investigated area. We modelled the final intrusion of the May 2008 – July 2009 flank eruption at Mt. Etna, whose onset was preceded by an intense seismic swarm and marked by ground deformation recorded at GPS stations. The future vent forecast highlights the area with higher probability, increasing the difference in relative values between that zone and the rest of the volcano edifice. It is worthy notice that a good accordance is evident if the highest probability area is compared with the real vent occurrence.231 19 - PublicationRestrictedK-CM application for supervised pattern recognition at Mt. Etna: an innovative tool to forecast flank eruptive activity(2019)
; ; ; ; ; ; ; ; ; ;; We investigated the relationship between the temporal monitoring series routinely recorded at Mt. Etna and the flank eruptions that occurred between January 2001 and April 2005 by the K-contractive map (K-CM) method pattern classifier with supervised learning. The reference dataset includes 28 variables and 1580 records collected over 52 months for a total of 301 eruptive days. A two-step analysis was performed. In the first step analysis, we used the 28 parameters of each day to recognize anomalies heralding a flank eruption. K-CM estimated a sensitivity higher than 95% and a specificity close to 100%. In the second step analysis, we considered each record comprising the 28 variables for 6 days as an input (for a total of 180 inputs) and the outcomes of the seventh day as an output to predict eruption or rest. In this case, K-CM showed sensitivity and specificity close to 98%and 100%, respectively. Results highlight the reliability of the K-CM method to build up a prediction algorithm able to alert the volcano experts a day before the occurrence of a potential flank eruption. The robustness of the two analyses was investigated by the behavior of the receiver operating characteristic curve. The relative area under the curve showed values close to 1, thus providing a valid measure of the performance of the classifier. Finally, a complete overview of the performance levels of the method used was explored analyzing the retrieved Molchan error diagram, in both cases, trajectories very close to the theoretical minimum.298 6 - PublicationOpen AccessVents Pattern Analysis at Etna volcano (Sicily, Italy).(2014-04-27)
; ; ; ; ; ;Alfonso, Brancato; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Giuseppina, Tusa; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Mauro, Coltelli; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cristina, Proietti; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Stefano, Branca; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; We evaluate clustering features at Etna volcano using a dataset of flank vents spanning last 4.0 ka.120 146 - PublicationOpen AccessProbability hazard map for future vent opening at Etna volcano (Sicily, Italy).(2014-12-15)
; ; ; ; ; ; ; ; ;Placido, Montalto; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Alfonso, Brancato; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Flavio, Cannavò; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Mauro, Coltelli; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Mario, Mattia; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Domenico, Patanè; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cristina, Proietti; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Danila, Scandura; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; ; ; ; The frequent flank eruptions occurrence at Mt. Etna lead to a high volcanic hazard that, linked to a population of nearly one million people dwell on its flanks, poses a high volcanic risk. In the framework of the project PON SIGMA (Integrated Cloud-Sensor System for Advanced Multirisk Management), we developed a near real-time computer-assisted analysis and probabilistic evaluations that provide the identification of the areas prone to the highest vent opening hazard. The use of a code such BET_EF (Bayesian Event Tree_Eruption Forecasting) provide us a long-term hazard map mainly based on the past behaviour of the Etna volcano. The near real-time additional seismic and ground deformation data allow the long-term hazard map switches into a short-term future vent opening one. The short-term future vent opening was computed starting from the evaluation of deformation field over Etna surface. Analytical inversion of deformation and seismic data is performed to find the parameters of a magmatic source in an elastic, isotropic and homogeneous half-space and forward model is performed to compute the displacement field over Etna surface. We modelled the final intrusion of the Mount Etna May 2008 eruption that was accompanied by a violent seismic swarm and marked by ground deformation recorded at GPS stations. Results suggest a good accordance between the higher probability area and the real vent occurrence.260 44 - PublicationRestrictedForecasting Eruptive Activity at Mt. Etna (Sicily): the May 2008 - July 2009 Case Study(2014-10-29)
; ; ; ; ; ; ;Alfonso, Brancato; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Giuseppina, Tusa; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Salvatore, Alparone; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Tommaso, Calatabiano; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Filippo, Greco; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Giuseppe, Salerno; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; ; Mt. Etna is one of the most active volcanoes on the Earth, and a population of almost one million of people settle on its flank. Volcanic activity mainly consists of effusive and explosive paroxysmal activity both from its summit craters and new vents opened on the flanks along NE and S rift zones. Many villages and areas have been repeatedly invaded by lava flows during the historical period, posing the volcano hazard vulnerability assessment as a key feature in volcanology. Nowadays, volcanic activity is explored and supervised by an integrating multi-parametric monitoring approach such as to retrieve quantitative probabilistic estimates at both short- and long-temporal window and forecast impending eruptive activity. A successfully application of this approach was achieved by Brancato et al. (2011, 2012) by applying the BET_EF probabilistic code (Bayesian Event Tree_Eruption Forecasting; Figure 1). In the framework of the European Project MEDiterranean Supersite Volcanoes (MED-SUV), we show the results of BET_EF application in the flank eruptive activity occurred at Mt. Etna between May 2008 and July 2009. An inventory of seismic data, bulk SO2 flux, and microgravity collected in the period May 2007 – May 2008, was processed in a novel scenario of concept of inertia time window. Processed data provided outcomes fundamental for making objective and/or false predictions. Results clearly show the fundamental role of the monitoring data in defining key-stages of eruptive sequences, based on identification of thresholds.189 20 - PublicationRestrictedQuantifying probabilities of eruption at a well-monitored active volcano: an application to Mt.Etna (Sicily, Italy),(2012-03)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Brancato, A.; Universita di Catania ;Gresta, S.; Universita di Catania ;Sandri, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Selva, J.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Marzocchi, W.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italia ;Alparone, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Andronico, D.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Bonforte, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Caltabiano, T.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cocina, O.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Corsaro, R. A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cristofolini, R.; Universita di Catania ;Di Grazia, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Distefano, G.; Universita di Catania ;Ferlito, C.; Universita di Catania ;Gambino, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Giammanco, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Greco, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Napoli, R.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Tusa, G.; Universita di Catania ;Viccaro, M.; Universita di Catania; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; At active volcanoes, distinct eruptions are preceded by complex and different precursory patterns; in addition, there are precursory signals that do not necessarily lead to an eruption. The main purpose of this paper is to present an unprecedented application of the recently developed code named BET_EF (Bayesian Event Tree_Eruption Forecasting) to the quantitative estimate of the eruptive hazard at Mt. Etna volcano. We tested the model for the case history of the July-August 2001 flank eruption. Anomalies in geophysical, geochemical and volcanological monitoring parameters were observed more than a month in advance of the effective onset of the eruption. As a consequence, eruption probabilities larger than 90% were estimated. An important feature of the application of BET_EF to Mt. Etna was the probabilistic estimate of opening vent locations. The methodology allowed a clear identification of assumptions and the monitoring of parameter thresholds and provided rational means for their revision if new data or information are incoming.598 34 - PublicationRestrictedReply to “Comments on the paper “A crustal-upper mantle model for southeastern Sicily (Italy) from the integration of petrologic and geophysical data” by Manuella et al. (2013)”(2014)
; ; ; ; ;Manuella, F. C.; Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Università di Catania ;Brancato, A.; Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Università di Catania ;Carbone, S.; Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Università di Catania ;Gresta, S.; Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Università di Catania; ; ; We reply to the comments of Beccaluva et al. (2013) on the paper “A crustal-upper mantle model for southeastern Sicily (Italy) from the integration of petrologic and geophysical data” by Manuella et al.(2013). We entirely reject their speculative comments and strongly confirm our viewpoint on the aged oceanic nature of the lithospheric basement of southeastern Sicily and its offshore area.306 90 - PublicationOpen AccessPattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)(2016-07)
; ; ; ; ;Brancato, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Buscema, P. M.; Semeion Research Center of Sciences of Communication ;Massini, G.; Semeion Research Center of Sciences of Communication ;Gresta, S.; Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Sezione di Scienze della Terra, Università di Catania; ; ; A volcano can be defined as a complex system, not least for the hidden clues related to its internal nature. Innovative models grounded in the Artificial Sciences, have been proposed for a novel pattern recognition analysis at Mt. Etna volcano. The reference monitoring dataset dealt with real data of 28 parameters collected between January 2001 and April 2005, during which the volcano underwent the July-August 2001, October 2002-January 2003 and September 2004-April 2005 flank eruptions. There were 301 eruptive days out of an overall number of 1581 investigated days. The analysis involved successive steps. First, the TWIST algorithm was used to select the most predictive attributes associated with the flank eruption target. During his work, the algorithm TWIST selected 11 characteristics of the input vector: among them SO2 and CO2 emissions, and also many other attributes whose linear correlation with the target was very low. A 5 × 2 Cross Valida- tion protocol estimated the sensitivity and specificity of pattern recognition algorithms. Finally, different classification algorithms have been compared to understand if this pattern recognition task may have suitable results and which algorithm performs best. Best results (higher than 97% accuracy) have been obtained after performing advanced Artificial Neural Networks, with a sensi- tivity and specificity estimates over 97% and 98%, respectively. The present analysis highlights that a suitable monitoring dataset inferred hidden information about volcanic phenomena, whose highly non-linear processes are enhanced.125 171 - PublicationOpen AccessNew evidence for the serpentinization of the Palaeozoic basement of southeastern Sicily from joint 3-D seismic velocity and attenuation tomographyIn this study, we derived the first 3-D P-wave seismic attenuation images (QP) as well as new 3-D VP and VP/VS models for the crust in southeastern Sicily.We used a large data set of local seismic events occurring in the time span 1994–2013. The results of this tomographic study have important implications on the seismic behaviour of the region. Based on velocity and attenuation images, we identified distinct volumes characterized by different fluid content, which correlate well with seismicity distribution. Moreover, the obtained velocity and attenuation tomographies help us to provide a more complete picture of the crustal structure of the area. High VP, high QP and high VP/VS values have been obtained in the crustal basement, below a depth of 8 km, and may be interpreted as due to the presence of serpentinized peridotites. Accordingly, the new model for the degree of serpentinization, retrieved from VP values, shows that the basement has an average serpentinization value of 96 ± 3 vol.% at 8 km, decreasing to 44 ± 5 vol.% at about 18–20 km. Our joint interpretation of geophysical and petrophysical evidence suggests that the nature and composition of the Hyblean upper lithosphere may differ from accepted and longestablished geological models, which consider this lithospheric block a continuation of the Africa continental plate.
242 235 - PublicationOpen AccessProcedura near real-time per la valutazione dell’hazard da eruzioni laterali all’Etna (Sicilia, Italia)(2017)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; L’Etna è uno stratovulcano composito situato lungo la costa ionica della Sicilia. Le frequenti eruzioni laterali (soprattutto lungo i Rift NE, S e O) fanno sì che ad esso sia associata una elevata pericolosità vulcanica. Se valutiamo anche l’elevato tasso di urbanizzazione dei suoi fianchi risulta evidente il notevole valore esposto al pericolo. Nel quadro del progetto PON SIGMA (Integrated Cloud-Sensor System for Advanced Multirisk Management), abbiamo sviluppato un’analisi in tempo quasi reale e completamente automatizzata volta alla valutazione delle aree soggette alla più alta probabilità di apertura di bocche effusive (vent) e il corrispondente hazard relativo all’accadimento di eruzioni effusive. L’algoritmo bayesiano BET_EF (Bayesian Event Tree_Eruption Forecasting), basato sull’albero degli eventi, è, nel nostro approccio, utilizzato inizialmente per la valutazione di una mappa di pericolosità a lungo termine sulla base dell’attività effusiva degli ultimi 4000 anni. L’analisi e l’inversione dei parametri monitorati in tempo reale, quali, ad esempio, dati sismici e sorgenti di tremore vulcanico, permette di valutare la funzione di densità di probabilità (PDF) a breve termine. Un’ulteriore applicazione dell’algoritmo BET_EF fornisce uno scenario, in termini di mappa di pericolosità, a breve termine per le simulazioni delle colate laviche. L’output della seconda applicazione del BET_EF costituisce l’input per simulare una serie di colate laviche e valutare il relativo hazard, definito in termini di impatto sul territorio. Allo scopo di testare limiti e utilità del nostro approccio integrato, abbiamo utilizzato, come test case, la fase intrusiva iniziale dell’eruzione laterale accaduta all’Etna nel maggio 2008. La previsione di apertura di vent evidenzia la zona con maggiore probabilità e, dall’analisi dei risultati, si nota un buon accordo tra l’area a probabilità più alta e la posizione effettiva del vent. È stata eseguita una serie di 200 simulazioni di colate per valutare le aree soggette a più alta probabilità di invasione lavica. Infine, è stata valutata la densità dei flussi simulati e i valori più alti sono risultati in accordo con l’area effettivamente coperta dal campo lavico dell’eruzione considerata.874 44