Annamaria Vicari

The initial phases of the 2008–2009 Mount Etna eruption: A multidisciplinary approach for hazard assessment

Accepted for publication in Journal of Geophysical Research. Copyright (2010) American Geophysical Union

Near‐real‐time forecasting of lava flow hazards during the 12–13 January 2011 Etna eruption

We are grateful to EUMETSAT for SEVIRI data, to NASA for MODIS data, and toNOAAfor AVHRR data. The authors thank one anonymous reviewer and V. Acocella for their helpful and constructive comments. This study was performed with the financial support from the V3‐LAVA project (INGV‐DPC 2007‐2009 contract).

Forecasting lava flow hazards during the 2006 Etna eruption: Using the MAGFLOW cellular automata model

The MAGFLOW cellular automata (CA) model was able to fairly accurately reproduce the time of the lava flow advance during the 2006 Etna eruption, leading to very plausible flow predictions. MAGFLOW is intended for use in emergency response situations during an eruption to quickly forecast the lava flow path over some time interval from the immediate future to a long-time forecast. Major discrepancies between the observed and simulated paths occurred in the early phase of the 2006 eruption due to an underestimation of the initial flow rate, and at the time of the overlapping with the 2004-2005 lava flow. Very good representations of the areas likely to be inundated by lava flows were obtained when we adopt a time-varying effusion rate and include the 2004-2005 lava flow field in the Digital Elevation Model (DEM) of topography.

Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results

Abstract. The forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonlinear. An alternative approach to this problem that does not use standard differential equation methods is Cellular Nonlinear Networks (CNNs). The CNN paradigm is a natural and flexible framework for describing locally interconnected, simple, dynamic systems that have a lattice-like structure. They consist of arrays of essentially simple, nonlinearly coupled dynamic circuits containing linear and non-linear elements able to process large amounts of information in real time. Two different approaches have been implemented in simulating some lava flows. Firstly, a typical technique of the CNNs to analyze spatio-temporal phenomena (as Autowaves) in 2-D and in 3-D has been utilized. Secondly, the CNNs have been used as solvers of partial differential equations of the Navier-Stokes treatment of Newtonian flow.

A texton-based cloud detection algorithm for MSG-SEVIRI multispectral images

A new statistical texton-based method for cloud detection through satellite image analysis is presented. The ultimate goal is to improve the performance of remote sensing techniques used to support the observations of active volcanic processes. The proposed method is a supervised classifier that exploits radiance spatial correlation in satellite images using a statistical descriptor of texture called texton. Cloudy and clear-sky models are determined using cluster analysis over the image features. The pixels to be classified are compared with the estimated models and assigned to the closest model. The cloud detection algorithm has been tested on a data set of MSG-SEVIRI images acquired during 2008 (about 35,000 images) of the Sicily area. Results show that the texton-based approach is robust in terms of percentage of correctly classified pixels, reaching more than 85% of success in both daytime and nighttime images.

Hot spot detection and effusion rate estimation using satellite data to drive lava flow simulations

We demonstrated how infrared satellite data can be used to drive numerical simulations of lava flow paths and produced a detailed chronology of lava flow emplacement while an eruptive event was ongoing. We evaluated the lava flow hazard on Etna volcano during the first 40 days of May 2008 eruption by means of the MAGFLOW cellular automata model. This model was developed for simulating lava flow paths and the temporal evolution of lava emplacement. Many data are necessary to run MAGFLOW and to determine how far lava will flow. However, for a given composition, the volumetric flux of lava from the vent (i.e. the lava effusion rate) is the principal parameter controlling final flow dimensions. Measuring effusion rates is therefore of great interest. To this end, we developed an automatic system that uses near-real-time infrared satellite data to estimate the lava effusion rates. Such system exploits the satellite data directly received and automatically processed by RST approach at CNR-IMAA, as input information for the prediction of the path lava flows. In particular, hotspots detected by RST, using both AVHRR and MODIS data, have been used to compute time-varying effusion rates, which have been applied to drive lava flow simulation using the original MAGFLOW cellular automata algorithm. Achieved results confirm the reliability of two methodologies (i.e. RST approach and MAGFLOW model), as well as the potential of the whole integrated processing chain, as an effective tool for real-time monitoring and mitigation of volcanic hazard.

A multidisciplinary strategy for in-situ and remote sensing monitoring of areas affected by pressurized fluids: Application to mud volcanoes: A multidisciplinary environmental monitoring strategy

A multidisciplinary strategy integrating a data set obtained using different mthods and techniques, ranging from remote sensing (UAV system, FTIR, thermal imaging) to direct field measurements (soil heat flux, soil CO2 flux, gravimetry and geomagnetism) proved highly capable of modeling regions affected by pressurized fluids circulation and extreme natural environments. As a test site, the Salinelle mud volcanoes area, located close to the city of Paternò (Sicily), was selected. This area is characterized by gas exhalations through water/mud vents. Detailed morpho-structural information, GIS thematic maps and geochemail signature of the released gas were quickly retrieved. This study showed that by integrating and harmonizing many disciplines of geosciences it is possible to get a comprehensive geological model of the studied area. Results, showed the accurate detection of structural setting of such an area and the opportunuty to monitor the spatial/temporal evolution of water/mud vents. The proposed approach allowed to expand the use of each single technique beyond its traditional applications and to make it a potential tool for many fields of geoscience.

Models of Lava Flow Through the CNN-Based E^3 Architecture

Many works investigated the phenomenon of lava flow through numerical models, obtaining excellent results, although most of the models require several approximations. Each model, in fact, has its restrictions: for instance, some of them work only on inclined planes, while others do not consider cooling processes associated to lava flow and so on. Simplifications are often needed to afford the computational effort required by the problem. Although the increasing computational capability of computers, due to technological progress, can be very useful to quickly resolve differential equations, which are essential to study lava flows, it is still important to take into account alternative solutions to the problem. One of these solutions is the use of parallel analog processors, namely cellular nonlinear networks (CNNs). In particular, the approach used in this work is based on the E^3 architecture (Caponetto et al., 2004) used for the first time to study lava flows. Two different models are proposed.