Search

Abstract The development of probabilistic maps associated with lava flow inundation is essential to assess hazard in open vent volcanoes, especially those that have highly urbanized flanks. In this study we present the new lava flow hazard map linked to the summit eruptions of Mt. Etna, which has been developed using a probabilistic approach that integrates statistical analyses of the volcanological historical data with numerical simulations of lava flows. The statistical analysis of volcanological data (including vent location, duration and lava volumes) about all summit eruptions occurred since 1998 has allowed us both to estimate the spatiotemporal probability of future vent opening and to extract the effusion rate curves for lava flow modelling. Numerical simulations were run using the GPUFLOW model on a 2022 Digital Surface Model derived from optical satellite images. The probabilistic approach has been validated through a back-analysis by calculating the fit between the expected probabilities of inundation and the lava flows actually emplaced during the 2020-2022 period. The obtained map shows a very high probability of inundation of lava flows emitted at vents linked to the South East Crater, according to the observation of the eruptive dynamics in the last decades.

Abstract Timely estimations of magma volumes emitted during an eruption or a sequence of explosive events are vital for investigating the eruptive activity and evaluating the associated hazard. A reliable method for estimating erupted volumes is based on the analysis of digital surface models that nowadays can be obtained subsequently using stereo or tri‐stereo optical satellite imagery. However, the real‐time estimation of the erupted volumes is still an open challenge. Here, we explore the capacity of extracting volume estimates from continuous measurements of volumetric strain changes recorded by borehole dilatometers. We compare the volumes derived from numerous high spatial resolution satellite images with high precision strain records at Etna during 2020–2022, when more than 60 lava fountains occurred. The good correlation between the two data sets shows that strain changes can be used as a proxy to estimate the emitted volumes both over time and in real‐time.

We hereby present VolcFire, a new cellular automaton model for fire propagation aimed at the creation of fire hazard maps for fires of volcanic origin. The new model relies on satellite-derived input data for the topography, land-use, fuel, and humidity information, and produces probabilistic maps of fire propagation simulating fire spread. The model contains several simplifications compared to the current state-of-the-art, limiting its usability to plan fire-fighting interventions during an event in favour of a reduced computational load. The accuracy and reliability of the model are also discussed by presenting its ability to reproduce two recent fires on Stromboli island, with good spatial fit (Brier score of 0.146±0.002 for the 3 July 2019 volcanic fire, and of 0.073±0.001 for the 25 May 2022 anthropogenic fire) and less than 1.5% variation across multiple simulations for the same event.

Between December 2020 and February 2022, the South East Crater of Etna has been the source of numerous eruptions, mostly characterized by the emission of lava fountains, pyroclastic material and short-lasting lava flows. Here we estimate the volume and distribution of the lava deposits by elaborating multi-source satellite imagery. SEVIRI data have been elaborated using CL-HOTSAT to estimate the lava volume emitted during each event and calculate the cumulative volume; Pléiades and WorldView-1 data have been used to derive Digital Surface Models, whose differences provide thickness distributions and hence volumes of the volcanic deposits. We find a good agreement, with the total average lava volume obtained by SEVIRI reaching 73.2 × 106 m3 and the one from optical data amounting to 67.7 × 106 m3. This proves the robustness of both techniques and the accuracy of the volume estimates, which provide important information on the lava flooding history and evolution of the volcano.

How (in)formal should the classic expression describing risk as the product of hazard, exposure, and vulnerability be considered? What would be the most complete way to describe the process of risk mitigation? These are the questions we try to answer here, using a formal, mathematically sound yet abstract description of hazard, exposure, vulnerability, and risk. We highlight the elements that can be affected for the purpose of mitigation and show how this can improve the quantitative assessment of the procedural aspects of risk mitigation, both long- and short-term, down to the timescale of emergency response.

We present a new model called GPUFLOW for the modeling and simulation of geophysical flows. GPUFLOW, which is based on the cellular automaton paradigm, features a physical model for the thermal and rheological evolution of lava flows (including temperature-dependent emissivity and cooling by radiation and air convection), support for debris flows without thermal dependency, a parallel implementation on graphic processing units (GPUs), and a simpler and computationally more efficient solution to the grid bias problem. Here, we describe the physical–mathematical model implemented in GPUFLOW and estimate the influence of input data on the flow emplacement through different synthetic test cases. We also perform a validation using two real applications: a debris flow that occurred in July 2006 in the Dolomites (Italy) and the December 2018 lava flow from the Etna volcano. GPUFLOW’s reliability prediction is accomplished by fitting the simulation with the actual flow fields, obtaining average values between ~55% and 75%.

The rate at which lava is discharged plays a key role in controlling the distance covered by lava flows from eruptive vents. We investigate the available time-averaged discharge rates (TADRs) estimated for recent flank eruptions at Mt. Etna volcano (Italy), in order to define a possible generalized effusion rate trend which is consistent with observed real data. Our analysis indicates a rapid waxing phase in which effusion rate peaks occur for between 0.5 and 29% of the total eruption time, followed by a progressive decrease in the waning phase. Three generalized curves are built by calculating the 25th, 50th and 75th percentiles values associated with the occurrence of effusion peaks, and with the slope variations of descending curves in the waning phase. The obtained curves are used as an input for the GPUFLOW model in order to perform numerical simulations of the lava flows paths on inclined planes, and are compared with those generated by using effusion rate curves with a bell-shaped time-distribution. Our tests show how these characteristic curves could impact single-vent scenarios, as well as short- and long-term hazard maps, with maximum variations of up to 40% for a specific category of eruptive events.

Spaceborne detection and measurements of high-temperature thermal anomalies enable monitoring and forecasts of lava flow propagation. The accuracy of such thermal estimates relies on the knowledge of input parameters, such as emissivity, which notably affects computation of temperature, radiant heat flux, and subsequent analyses (e.g., effusion rate and lava flow distance to run) that rely on the accuracy of observations. To address the deficit of field and laboratory-based emissivity data for inverse and forward modelling, we measured the emissivity of ‘a’a lava samples from the 2001 Mt. Etna eruption, over the wide range of temperatures (773 to 1373 K) and wavelengths (2.17 to 21.0 µm). The results show that emissivity is not only wavelength dependent, but it also increases non-linearly with cooling, revealing considerably lower values than those typically assumed for basalts. This new evidence showed the largest and smallest increase in average emissivity during cooling in the MIR and TIR regions (~30% and ~8% respectively), whereas the shorter wavelengths of the SWIR region showed a moderate increase (~15%). These results applied to spaceborne data confirm that the variable emissivity-derived radiant heat flux is greater than the constant emissivity assumption. For the differences between the radiant heat flux in the case of variable and constant emissivity, we found the median value is 0.06, whereas the 25th and the 75th percentiles are 0.014 and 0.161, respectively. This new evidence has significant impacts on the modelling of lava flow simulations, causing a dissimilarity between the two emissivity approaches of ~16% in the final area and ~7% in the maximum thickness. The multicomponent emissivity input provides means for ‘best practice’ scenario when accurate data required. The novel approach developed here can be used to test an improved version of existing multi-platform, multi-payload volcano monitoring systems.

The formation of new craters or cones in active volcanoes can influence the style and frequency of eruptions and may also significantly affect volcanic hazard. Here, we explore how the lava flow hazard changed on Mount Etna in the past 20 years, while continuous shifts in the location of volcanism in the summit area were experienced. The summit area of Mount Etna has undergone large morphological variations since the formation of the South-East Crater (SEC) in 1971 and New Southeast Crater (NSEC) in 2007, which have also marked a notable change in the eruptive activity of the volcano. Indeed, in less than 50 years, the SEC area has been the source of more than two hundreds of eruptions characterized by the emission of lava fountains, pyroclastic material, and short-lasting lava flows, which are the greatest hazard presented to the tourist facilities on the flanks of Etna. Here we statistically analyze the main quantitative parameters (i.e., duration and lava volume) of 154 well-documented eruptive events occurred since 1998 with the aim of characterizing the time-volume evolution of the SEC activity. From 1998 to 2018, we find a cumulative volume of ∼135 millions of cubic meters that gives a long-term output rate of ∼0.21 m3/s, with a remarkable change in the eruptive style (longer and more voluminous events) starting from 2011. Taking advantage of this analysis and lava flow simulations run on a Pléiades-derived topography updated to 2016, we also present the new hazard map from lava flow inundation in the SEC area, which is quite different from the hazard map produced in 2011, covering an area of ∼15 km2 and reaching the lowest altitude of about 1,500 m a.s.l. This map establishes an important basis for mitigation decisions and long-term territorial planning, allowing key at risk areas to be rapidly and appropriately identified.

Lava flow mapping has direct relevance to volcanic hazards once an eruption has begun. Satellite remote sensing techniques are increasingly used to map newly erupted lava, thanks to their capability to survey large areas with frequent revisit time and accurate spatial resolution. Visible and infrared satellite data are routinely used to detect the distributions of volcanic deposits and monitor thermal features, even if clouds are a serious obstacle for optical sensors, since they cannot be penetrated by optical radiation. On the other hand, radar satellite data have been playing an important role in surface change detection and image classification, being able to operate in all weather conditions, although their use is hampered by the special imaging geometry, the complicated scattering process, and the presence of speckle noise. Thus, optical and radar data are complementary data sources that can be used to map lava flows effectively, in addition to alleviating cloud obstruction and improving change detection performance. Here, we propose a machine learning approach based on the Google Earth Engine (GEE) platform to analyze simultaneously the images acquired by the synthetic aperture radar (SAR) sensor, on board of Sentinel-1 mission, and by optical and multispectral sensors of Landsat-8 missions and Multi-Spectral Imager (MSI), on board of Sentinel-2 mission. Machine learning classifiers, including K-means algorithm (K-means) and support vector machine (SVM), are used to map lava flows automatically from a combination of optical and SAR images. We describe the operation of this approach by using a retrospective analysis of two recent lava flow-forming eruptions at Mount Etna (Italy) and Fogo Island (Cape Verde). We found that combining both radar and optical imagery improved the accuracy and reliability of lava flow mapping. The results highlight the need to fully exploit the extraordinary potential of complementary satellite sensors to provide time-critical hazard information during volcanic eruptions.

Between 28 March and 1 April 2020, Stromboli volcano erupted, with overflows from the NE crater rim spreading along the barren Sciara del Fuoco slope and reaching the sea along the NW coast of the island. Poor weather conditions did not allow a detailed observation of the crater zone through the cameras monitoring network, but a clear view of the lower slope and the flows expanding in the area allowed us to characterize the flow features. This evidence was integrated with satellite, GBInSAR, and seismic data, thus enabling a reconstruction of the whole volcanic event, which involved several small collapses of the summit cone and the generation of pyroclastic density currents (PDCs) spreading along the slope and on the sea surface. Satellite monitoring allowed for the mapping of the lava flow field and the quantification of the erupted volume, and GBInSAR continuous measurements detected the crater widening and the deflation of the summit cone caused by the last overflow. The characterization of the seismicity made it possible to identify the signals that are associated with the propagation of PDCs along the volcano flank and, for the first time, to recognize the signal that is produced by the impact of the PDCs on the coast.

Detecting, locating and characterizing volcanic eruptions at an early stage provides the best means to plan and mitigate against potential hazards. Here, we present an automatic system which is able to recognize and classify the main types of eruptive activity occurring at Mount Etna by exploiting infrared images acquired using thermal cameras installed around the volcano. The system employs a machine learning approach based on a Decision Tree tool and a Bag of Words-based classifier. The Decision Tree provides information on the visibility level of the monitored area, while the Bag of Words-based classifier detects the onset of eruptive activity and recognizes the eruption type as either explosion and/or lava flow or plume degassing/ash. Applied in real-time to each image of each of the thermal cameras placed around Etna, the proposed system provides two outputs, namely, visibility level and recognized eruptive activity status. By merging these outcomes, the monitored phenomena can be fully described from different perspectives to acquire more in-depth information in real time and in an automatic way.

The Etna flank eruption that started on 24 December 2018 lasted a few days and involved the opening of an eruptive fissure, accompanied by a seismic swarm and shallow earthquakes, significant SO2 flux release, and by large and widespread ground deformation, especially on the eastern flank of the volcano. Lava fountains and ash plumes from the uppermost eruptive fissure accompanied the opening stage, causing disruption to Catania International Airport, and were followed by a quiet lava effusion within the barren Valle del Bove depression until 27 December. This was the first flank eruption to occur at Etna in the last decade, during which eruptive activity was confined to the summit craters and resulted in lava fountains and lava flow output from the crater rims. In this paper, we used ground and satellite remote sensing techniques to describe the sequence of events, quantify the erupted volumes of lava, gas, and tephra, and assess volcanic hazards.

Estimates of lava volumes provide important data on the lava flooding history and evolution of a volcano. For mapping volcanic deposits, including lava flows, the advancement of satellite remote sensing techniques offers a great potential. Here we characterize the eruptive events occurred at Mt Etna between January 2011 and December 2015 leading to the emplacement of numerous lava flows and to the formation of a new pyroclastic cone (NSEC) on the eastern flank of the South East Crater. The HOTSAT system is used to analyze remote sensing data acquired by the SEVIRI sensor in order to detect the thermal anomalies from active lava flows and calculate the associated radiative power. The time-series analysis of SEVIRI data provides an estimation of event magnitude and intensity of the effusive material erupted during each event. The cumulative volume estimated from SEVIRI images from 2011 to 2015 adds up to ~106 millions of cubic meters of lava, with a time-averaged rate of ~0.68 m3 s−1. This estimate is independently supported and bounded using a topographic approach, i.e., by subtracting the last topography of Etna updated to 2005 from a 2015 digital elevation model (DEM), produced using tri-stereo Pléiades satellite images acquired on December 18, 2015. The total volume of products erupted from 2005 to 2015, calculated from topography difference by integration of the thickness distribution over the area covered, is about 287 × 106 m3, of which ~55 × 106 m3 is the volume of the NSEC cone. This 10-year volume is below the typical erupted volumes on decadal scale at Mt Etna, interrupting its stable and resilient output trend.

Accurate mapping of recent lava flows can provide significant insight into the development of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical issues (e.g., the difficulty to survey a spatially extended lava flow with either aerial or ground instruments while avoiding hazardous locations). The huge amount of moderate to high resolution multispectral satellite data currently provides new opportunities for monitoring of extreme thermal events, such as eruptive phenomena. While retrieving boundaries of an active lava flow is relatively straightforward, problems arise when discriminating a recently cooled lava flow from older lava flow fields. Here, we present a new supervised classifier based on machine learning techniques to discriminate recent lava imaged in the MultiSpectral Imager (MSI) onboard Sentinel-2 satellite. Automated classification evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number, reflectance, radiance) into a specified number of classes. Bands at the spatial resolution of 10 m (bands 2, 3, 4, 8) are used as input to the classifier. The training phase is performed on a small number of pixels manually labeled as covered by fresh lava, while the testing characterizes the entire lava flow field. Compared with ground-based measurements and actual lava flows of Mount Etna emplaced in 2017 and 2018, our automatic procedure provides excellent results in terms of accuracy, precision, and sensitivity.

Satellite-derived data, including an estimation of the eruption rate, proximal volcanic deposits and lava flow morphometric parameters (area, maximum length, thickness, and volume) are provided for the eruption that occurred at Mt Etna on 6–8 December 2015. This eruption took place at the New Southeast Crater (NSEC), the youngest of the summit craters of Etna, shortly after a sequence of four violent paroxysmal events took place in 65 h (3–5 December) at “Voragine”, the oldest summit crater. Multispectral SEVIRI images at 15 min sampling time have been used to compute time-averaged eruption rate curves, while tri-stereo Pléiades images, at 50 cm spatial resolution, provided the pre-eruptive topography and topographic changes due to volcanic deposits. In addition to the two types of satellite data, other parameters have been inferred, such as probable vesicularity and pyroclastic deposits.

The huge amount of information coming from remote sensors on satellites has allowed monitoring changes in the planetary environment from about 50 years. These instruments are widely adopted to observe extreme thermal events such as eruptive phenomena in volcanic areas. Although the availability of so many different infrared sensors makes these instruments suitable to observe different kind of thermal phenomena, choosing the right infrared sensor to monitor each thermal event is not straightforward. In fact, the decision should take into account both the main features of the phenomena under investigation, e.g., its size and temperatures, that are often not known a priori, and the instruments specifications, e.g., spatial resolution. Here, a smart decision support system (SDSS) is proposed to address this task. In particular, we used a SDSS to simulate remote sensors responses, collect data coming from three different classes of remote sensors, retrieve information about the main features of the observed thermal event and, consequently, select the most suitable infrared remote sensor for the specific observed phenomena. Results obtained for a real case of study at Etna volcano is shown.

In surgical knee replacement, the damaged knee joint is replaced with artificial prostheses. An accurate clinical evaluation must be carried out before applying knee prostheses to ensure optimal outcome from surgical operations and to reduce the probability of having long-term problems. Useful information can be inferred from estimates of the stress acting onto the bone-prosthesis system of the knee joint. This information can be exploited to tailor the prosthesis to the patient’s anatomy. We present a compound system for pre-operative surgical planning based on structural simulation of the bone-prosthesis system, exploiting patient-specific data.

Affine Arithmetic (AA) is a self-verifying computational approach that keeps track of first-order correlation between uncertainties in the data and intermediate and final results.In this paper we propose a higher-order extension satisfying the requirements of genericity, arbitrary-order and self-verification, comparing the resulting ethod with other well-known high-order extensions of AA.

The MAGFLOW lava simulation model is a cellular automaton developed by the Sezione di Catania of the Istituto Nazionale di Geofisica e Vulcanologia (INGV) and it represents the peak of the evolution of cell-based models for lava-flow simulation. The accuracy and adherence to reality achieved by the physics-based cell evolution of MAGFLOW comes at the cost of significant computational times for long-running simulations. The present study describes the efforts and results obtained by porting the original serial code to the parallel computational platforms offered by modern video cards, and in particular to the NVIDIA Compute Unified Device Architecture (CUDA). A number of optimization strategies that have been used to achieve optimal performance on a graphic processing units (GPU) are also discussed. The actual benefits of running on the GPU rather than the central processing unit depends on the extent and duration of the simulated event; for large, long-running simulations, the GPU can be 70-to-80-times faster, while for short-lived eruptions with a small extents the speed improvements obtained are 40-to-50 times.

Satellite data, radiative power of hot spots as measured with remote sensing, historical records, on site geological surveys, digital elevation model data, and simulation results together provide a massive data source to investigate the behavior of active volcanoes like Mount Etna (Sicily, Italy) over recent times. The integration of these heterogeneous data into a coherent visualization framework is important for their practical exploitation. It is crucial to fill in the gap between experimental and numerical data, and the direct human perception of their meaning. Indeed, the people in charge of safety planning of an area need to be able to quickly assess hazards and other relevant issues even during critical situations. With this in mind, we developed LAV@HAZARD, a web-based geographic information system that provides an interface for the collection of all of the products coming from the LAVA project research activities. LAV@HAZARD is based on Google Maps application programming interface, a choice motivated by its ease of use and the user-friendly interactive environment it provides. In particular, the web structure consists of four modules for satellite applications (time-space evolution of hot spots, radiant flux and effusion rate), hazard map visualization, a database of ca. 30,000 lava-flow simulations, and real-time scenario forecasting by MAGFLOW on Compute Unified Device Architecture.

First-order moving least-squares are typically used in conjunction with smoothed particle hydrodynamics in the form of post-processing filters for density fields, to smooth out noise that develops in most applications of smoothed particle hydrodynamics. We show how an approach based on higher-order moving least-squares can be used to correct some of the main limitations in gradient and second-order derivative computation in classic smoothed particle hydrodynamics formulations. With a small increase in computational cost, we manage to achieve smooth density distributions without the need for post-processing and with higher accuracy in the computation of the viscous term of the Navier–Stokes equations, thereby reducing the formation of spurious shockwaves or other streaming effects in the evolution of fluid flow. Numerical tests on a classic two-dimensional dam-break problem confirm the improvement of the new approach.

We have developed a robust and scalable multi-GPU (Graphics Processing Unit) version of the cellular-automaton-based MAGFLOW lava simulator. The cellular automaton is partitioned into strips that are assigned to different GPUs, with minimal overlapping. For each GPU, a host thread is launched to manage allocation, deallocation, data transfer and kernel launches; the main host thread coordinates all of the GPUs, to ensure temporal coherence and data integrity. The overlapping borders and maximum temporal step need to be exchanged among the GPUs at the beginning of every evolution of the cellular automaton; data transfers are asynchronous with respect to the computations, to cover the introduced overhead. It is not required to have GPUs of the same speed or capacity; the system runs flawlessly on homogeneous and heterogeneous hardware. The speed-up factor differs from that which is ideal (#GPUs×) only for a constant overhead loss of about 4E−2 · T · #GPUs, with T as the total simulation time.

A smoothed particle hydrodynamics (SPH) method for lava-flow modeling was implemented on a graphical processing unit (GPU) using the compute unified device architecture (CUDA) developed by NVIDIA. This resulted in speed-ups of up to two orders of magnitude. The three-dimensional model can simulate lava flow on a real topography with free-surface, non-Newtonian fluids, and with phase change. The entire SPH code has three main components, neighbor list construction, force computation, and integration of the equation of motion, and it is computed on the GPU, fully exploiting the computational power. The simulation speed achieved is one to two orders of magnitude faster than the equivalent central processing unit (CPU) code. This GPU implementation of SPH allows high resolution SPH modeling in hours and days, rather than in weeks and months, on inexpensive and readily available hardware.

Le MatematicheVol. LXIII (2008 - Fasc. I, pp. 15–30)For a technical mistake this article appeared without references and citations. Here we add the references and, page by page, the necessary citations in order of appearance. We apologize for this inconvenient.