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Fujita, E.
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Fujita, E.
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- PublicationOpen AccessIntegration of stochastic models for long-term eruption forecasting into a Bayesian event tree scheme: a basis method to estimate the probability of volcanic unrest(2013-02-12)
; ; ; ;Garcia-Aristizabal, A.; AMRA ;Selva, J.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Fujita, E.; National Research Institute for Earth Science and Disaster Prevention; ; Eruption forecasting refers, in general, to the assessment of the occurrence probability of a given erup- tive event, whereas volcanic hazards are normally associated with the analysis of superficial and evident phenomena that usually accompany eruptions (e.g., lava, pyroclastic flows, tephra fall, lahars, etc.). Nevertheless, several hazards of volcanic origin may occur in noneruptive phases dur- ing unrest episodes. Among others, remarkable examples are gas emissions, phreatic explosions, ground deforma- tion, and seismic swarms. Many of such events may lead to significant damages, and for this reason, the “risk” associ- ated to unrest episodes could not be negligible with respect to eruption-related phenomena. Our main objective in this paper is to provide a quantitative framework to calculate probabilities of volcanic unrest. The mathematical frame- work proposed is based on the integration of stochastic mod- els based on the analysis of eruption occurrence catalogs into a Bayesian event tree scheme for eruption forecast- ing and volcanic hazard assessment. Indeed, such models are based on long-term eruption catalogs and in many cases allow a more consistent analysis of long-term tem- poral modulations of volcanic activity. The main result of this approach is twofold: first, it allows to make inferences about the probability of volcanic unrest; second, it allows to project the results of stochastic modeling of the eruptive history of a volcano toward the probabilistic assessment of volcanic hazards. To illustrate the performance of the pro- posed approach, we apply it to determine probabilities of unrest at Miyakejima volcano, Japan.259 459 - PublicationRestrictedA quantitative approach for evaluating lava flow simulation reliability: LavaSIM code applied to the 2001 Etna eruption(2009-09-09)
; ; ; ; ;Proietti, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Coltelli, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Marsella, M.; La Sapienza Università di Roma ;Fujita, E.; National Research Institute for Earth Science and Disaster Prevention; ; ; This work developed a quantitative approach for evaluating the reliability of lava flow simulation codes. In particular, it applied the LavaSIM code to simulate the main lava flow emplaced on the south flank of Mount Etna (Italy) between 18 July and 9 August 2001 which represents an ideal test case for validating numerical codes. LavaSIM is the only full 3-D model and is thus able to account for the vertical variation of lava properties such as temperature, viscosity, velocity, and liquidus or solidus state. It presents the most complete description of the lava cooling, and its greatest peculiarity is the potential to discriminate between cells filled by liquid or solid lava. Thirteen simulation tests were performed varying the main input parameters, and they were checked thanks to the availability of syneruption maps, defining the lava flow planar expansion throughout its whole emplacement. Two parameters were adopted for quantitatively evaluate the agreement between real and simulated flows: the percent length ratio (PLR), here defined, and the fitness function (e1). Their joint analysis allowed checking both the simulated lateral spreading, through e1, and the flow lengthening, through PLR. The simulated flows follow a path very similar to the observed one, giving a good fitting of the lateral spreading, though the simulations are, after the second day, normally longer and thinner. The temporal evolution of the three-dimensional distribution of liquid lava and crust was also analyzed. Finally, the analysis presented here demonstrated the great capability of the LavaSIM simulation code.242 20 - PublicationOpen AccessA quantitative approach for evaluating lava flow simulation reliability: the LavaSIM code applied to the 2001 Etna’s eruption(2009-06-11)
; ; ; ; ;Proietti, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Coltelli, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Marsella, M.; DITS - La Sapienza Università di Roma ;Fujita, E.; National Research Institute for Earth Science and Disaster Prevention, Japan; ; ; Many numerical codes have been developed to simulate the emplacement of lava flows for evaluating their possible evolutions and for defining, by a statistical approach, hazard maps useful for risk assessment and land planning. Although many examples of lava flow simulation can be found in literature, just a few of them attempted to quantify the correspondence between observed and simulated flows, nevertheless this is a crucial point especially if the codes are applied in real-time for risk managing. The aim of this work was to define a methodology to quantitatively evaluate the reliability of simulation codes. In particular, it applied the LavaSIM code (Hidaka et al., 2005) to simulate the main lava flow emplaced on the South flank of Mt. Etna (Italy) between 18 July and 9 August 2001 which represents an ideal test case for validating numerical codes (Coltelli et al., 2007). It is a single flow both for its geometry and its temporal evolution and, many data are available to be used as input of the simulations (lava composition, pre- and post-eruption topographies, final flow volume and thickness and temporal evolution of average volumetric flow rates) and for checking their results (2D temporal evolution). LavaSIM is the only full 3D model, thus able to account for the vertical variation of lava properties (temperature, viscosity, velocity and liquidus or solidus state). It is based on the 3D solution of the Navier-Stokes and the energy conservation equations and provides the most complete description of the lava cooling by considering radiation, conduction and convection. Its greatest peculiarity is to take into account crust formation by evaluating the enthalpy of every cell and by adopting an empiric threshold parameter (the solidification fraction of liquidity loss) to discriminate liquid and solid cells. Different values of input parameters (viscosity, solidification fraction of liquidity loss, eruptive enthalpy and lava emissivity) have been adopted for evaluating their influence on the simulated lava distribution and cooling. A simulation with constant lava discharge, averaged on the whole eruption, was also run for checking how the feeding affects the lava spreading and cooling. The results were first analyzed by comparing the planar expansions of real and simulated flows. A quantitative analysis was then carried out adopting two parameters for constraining both the lengthening and the planar expansion. For quantitatively verifying the correspondence between simulated and observed lengths, the Percent Length Ratio (PLR) was defined as the percentage ratio between simulated and observed lengths measured along the main flow direction. The second control parameter was the fitness function (e1) defined by Spataro et al. (2004) as the square root of the ratio between the intersection and the union of real and simulated areas. Since the e1 factor allows quantifying the simulated lateral spreading while PLR the flow lengthening, it is important to jointly analyze these two parameters. This work showed that by combining the fitness function of Spataro et al. (2004) with the Percent Length Ratio, here defined, it is possible to constrain both the lateral spreading (by e1) and the flow lengthening (by the PLR). The analysis here presented also demonstrated the capability of the LavaSIM simulation code to account for the vertical variation of the lava properties and to simulate the crust formation.188 190 - PublicationRestrictedConclusion: recommendations and findings of the RED SEED working group(The Geological Society of London, 2016)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Harris, A. J. L. ;Carn, S. ;Dehn, J. ;Del Negro, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Gudmundsson, M. T. ;Cordonnier, B. ;Barnie, T. ;Chahi, E. ;Calvari, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Catry, T. ;De Groeve, T. ;Coppola, D. ;Davies, A. ;Favalli, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Pisa, Pisa, Italia ;Ferrucci, F. ;Fujita, E. ;Ganci, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Garel, F. ;Huet, P. ;Kauahikaua, J. ;Kelfoun, K. ;Lombardo, V.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Macedonio, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;Pacheco, J. ;Patrick, M. ;Pergola, N. ;Ramsey, M. ;Rongo, R. ;Sahy, F. ;Smith, K. ;Tarquini, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Pisa, Pisa, Italia ;Thordarson, T. ;Villeneuve, N. ;Webley, P. ;Wright, R. ;Zaksek, K. ; ; ;; ; ; ; ;; ; ; ; ;; ; ;; ; ; ; ;; ; ; ; ; ; ; ; ;; ; ; ; ; ;; ; ; ; ;Harris, A. J. L. ;De Groeve, T. ;Garel, F.Carn, S. A.RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each pre- sentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive erup- tion onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.308 47 - PublicationOpen AccessA Brownian Model for Recurrent Volcanic Eruptions: an Application to Miyakejima Volcano (Japan)(2012-03)
; ; ; ;Garcia-Aristizabal, A.; Center for the Analysis and Monitoring of Environmental Risk (AMRA) ;Marzocchi, W.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italia ;Fujita, E.; National Research Institute for Earth Science and Disaster Prevention (NIED); ; The definition of probabilistic models as mathematical structures to describe the response of a volcanic system is a plausible approach to characterize the temporal behavior of volcanic eruptions, and constitutes a tool for long-term eruption forecasting. This kind of approach is motivated by the fact that volcanoes are complex systems in which a com- pletely deterministic description of the processes preceding eruptions is practically impos- sible. To describe recurrent eruptive activity we apply a physically-motivated probabilistic model based on the characteristics of the Brownian passage-time (BPT) distribution; the physical process defining this model can be described by the steady rise of a state variable from a ground state to a failure threshold; adding Brownian perturbations to the steady load- ing produces a stochastic load-state process (a Brownian relaxation oscillator) in which an eruption relaxes the load state to begin a new eruptive cycle. The Brownian relaxation os- cillator and Brownian passage-time distribution connect together physical notions of unob- servable loading and failure processes of a point process with observable response statistics. The Brownian passage-time model is parameterized by the mean rate of event occurrence, μ , and the aperiodicity about the mean, α . We apply this model to analyze the eruptive his- tory of Miyakejima volcano, Japan, finding a value of 44.2(±6.5 years) for the μ parameter and 0.51(±0.01) for the (dimensionless) α parameter. The comparison with other models often used in volcanological literature shows that this pysically-motivated model may be a good descriptor of volcanic systems that produce eruptions with a characteristic size. BPT is clearly superior to the exponential distribution and the fit to the data is comparable to other two-parameters models. Nonetheless, being a physically-motivated model, it provides an insight into the macro-mechanical processes driving the system.383 603 - PublicationRestrictedTesting a geographical information system for damage and evacuation assessment during an effusive volcanic crisis(2015-11-06)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Latutrie, B.; Laboratoire Magmas et Volcans, Université Blaise Pascal – CNRS – IRD, OPGC, 5 rue Kessler, 63038 Clermont Ferrand, France ;Andredakis, I.; Institute for the Protection and Security of the Citizen, European Commission – Joint Research Centre, Via E. Fermi, I-21027 Ispra (VA), Italy ;De Groeve, T.; Institute for the Protection and Security of the Citizen, European Commission – Joint Research Centre, Via E. Fermi, I-21027 Ispra (VA), Italy ;Harris, A.; Laboratoire Magmas et Volcans, Université Blaise Pascal – CNRS – IRD, OPGC, 5 rue Kessler, 63038 Clermont Ferrand, France ;Langlois, E.; Centre d'Etudes et de Recherches Appliquées au Massif Central, Université Blaise Pascal, 4 rue Ledru, 63057 Clermont-Ferrand, France ;van Wyk de Vries, B.; Laboratoire Magmas et Volcans, Université Blaise Pascal – CNRS – IRD, OPGC, 5 rue Kessler, 63038 Clermont Ferrand, France ;Saubin, E.; Laboratoire Magmas et Volcans, Université Blaise Pascal – CNRS – IRD, OPGC, 5 rue Kessler, 63038 Clermont Ferrand, France ;Bilotta, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cappello, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Crisci, G. M.; Department of Biology, Ecology and Earth Sciences, University of Calabria, Arcavacata, 87036 – Rende (CS), Italy ;D'Ambrosio, D.; Department of Mathematics and Computer Science, University of Calabria, Arcavacata, 87036 – Rende (CS), Italy ;Del Negro, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Favalli, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Pisa, Pisa, Italia ;Fujita, E.; National Research Institute for Earth Science and Disaster Prevention, Tennodai 3-1, Tsukuba, Ibaraki, 305-0006, Japan ;Iovine, G.; Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, 87036 – Rende (CS), Italy ;Kelfoun, K.; Laboratoire Magmas et Volcans, Université Blaise Pascal – CNRS – IRD, OPGC, 5 rue Kessler, 63038 Clermont Ferrand, France ;Rongo, R.; Department of Mathematics and Computer Science, University of Calabria, Arcavacata, 87036 – Rende (CS), Italy ;Spataro, W.; Department of Mathematics and Computer Science, University of Calabria, Arcavacata, 87036 – Rende (CS), Italy ;Tarquini, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Pisa, Pisa, Italia ;Coppola, D.; Dipartimento di Scienze della Terra, Università di Torino, Via Valperga Caluso, 35–10125 Torino, Italy ;Ganci, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Marchese, F.; Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Abientale C. da S. Loja, Zona Industriale, 85050 Tito Scalo (Pz), Italy ;Pergola, N.; Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Abientale C. da S. Loja, Zona Industriale, 85050 Tito Scalo (Pz), Italy ;Tramutoli, V.; Università della Basilicata, Scuola di Ingegneria Viale dell'Ateneo Lucano, 10, 85100 Potenza, Italy; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; Using two hypothetical effusive events in the Chaîne des Puys (Auvergne, France), we tested two geographical information systems (GISs) set up to allow loss assessment during an effusive crisis. The first was a local system that drew on all immediately available data for population, land use, communications, utility and building type. The second was an experimental add-on to the Global Disaster Alert and Coordination System (GDACS) global warning system maintained by the Joint Research Centre (JRC) that draws information from open-access global data. After defining lava-flow model source terms (vent location, effusion rate, lava chemistry, temperature, crystallinity and vesicularity), we ran all available lava-flow emplacement models to produce a projection for the likelihood of impact for all pixels within the GIS. Next, inundation maps and damage reports for impacted zones were produced, with those produced by both the local system and by GDACS being in good agreement. The exercise identified several shortcomings of the systems, but also indicated that the generation of a GDACS-type global response system for effusive crises that uses rapid-response model projections for lava inundation driven by real-time satellite hotspot detection – and open-access datasets – is within the current capabilities of the community.278 57