Amato, Eleonora

The integration of artificial intelligence (AI) into computational fluid dynamics (CFD) has significantly expanded the scope of fluid modeling, allowing enhanced analysis capabilities and improved simulation performance. While Eulerian methods already benefit extensively from AI, notably in reliable weather prediction, the application of AI to Lagrangian methods remains less consolidated. Smoothed particle hydrodynamics (SPH) is a Lagrangian mesh-less numerical method for CFD with well-established advantages for the simulation of highly dynamic free-surface flows. Here, we explore an application of AI to SPH simulations, utilizing an artificial neural network (ANN) to estimate hydrodynamic forces between particle pairs, learning from SPH-simulated results. A model of this nature, which emulates the mathematical representation of physics, is termed an emulator. We examine the physical significance of the emulator, presenting its applications in benchmark tests, assessing its faithfulness to traditional SPH simulations, and highlighting its ability to generalize and simulate test cases with varying levels of complexity beyond its training data.

In a volcanic context, the spectral response of lava flows depends on several endogenous and exogenous factors, like chemical composition, passing of years and weathering. A deeper knowledge about lava properties can be inferred by investigating their spectral response in satellite images. Here, we compare the spectral response of lava in time, physical, and chemical characteristics, inspecting visible to infrared high spatial resolution ESA Sentinel-2 satellite images of different volcanoes. Our results show increasing and decreasing patterns of the lava spectral response as a function of time and physicochemical composition.

The latest generation of high-spatial-resolution satellites produces measurements of high-temperature volcanic features at global scale, which are valuable to monitor volcanic activity. Recent advances in technology and increased computational resources have resulted in an extraordinary amount of monitoring data, which can no longer be so readily examined. Here, we present an automatic detection algorithm based on a deep convolutional neural network (CNN) that uses infrared satellite data to automatically determine the presence of volcanic thermal activity. We exploit the potentiality of the transfer learning technique to retrain a pre-trained SqueezeNet CNN to a new domain. We fine-tune the weights of the network over a new dataset opportunely created with images related to thermal anomalies of different active volcanoes around the world. Furthermore, an ensemble approach is employed to enhance accuracy and robustness when compared to using individual models. We chose a balanced training dataset with two classes, one containing volcanic thermal anomalies (erupting volcanoes) and the other containing no thermal anomalies (non-erupting volcanoes), to differentiate between volcanic scenes with eruptive and non-eruptive activity. We used satellite images acquired in the infrared bands by ESA Sentinel-2 Multispectral Instrument (MSI) and NASA & USGS Landsat 8 Operational Land Imager and Thermal InfraRed Sensor (OLI/TIRS). This deep learning approach makes the model capable of identifying the appearance of a volcanic thermal anomaly in the images belonging to the volcanic domain with an overall accuracy of 98.3%, recognizing the scene with active flows and erupting vents (i.e., eruptive activity) and the volcanoes at rest. This model is generalizable, and has the capability to analyze every image captured by these satellites over volcanoes around the world.

Volcanic explosive eruptions inject several different types of particles and gasses into the atmosphere, giving rise to the formation and propagation of volcanic clouds. These can pose a serious threat to the health of people living near an active volcano and cause damage to air traffic. Many efforts have been devoted to monitor and characterize volcanic clouds. Satellite infrared (IR) sensors have been shown to be well suitable for volcanic cloud monitoring tasks. Here, a machine learning (ML) approach was developed in Google Earth Engine (GEE) to detect a volcanic cloud and to classify its main components using satellite infrared images. We implemented a supervised support vector machine (SVM) algorithm to segment a combination of thermal infrared (TIR) bands acquired by the geostationary MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). This ML algorithm was applied to some of the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022. We found that the ML approach using a combination of TIR bands from the geostationary satellite is very efficient, achieving an accuracy of 0.86, being able to properly detect, track and map automatically volcanic ash clouds in near real-time.

Open conduit volcanoes like Stromboli can display elusive changes in activity before major eruptive events. Starting on December 2020, Stromboli volcano displayed an increasing eruptive activity, that on 19 May 2021 led to a crater-rim collapse, with pyroclastic density currents (PDCs) that spread along the barren NWflank, entered the sea and ran across it for more than 1 km. This episode was followed by lava flow output from the crater rim lasting a few hours, followed by another phase of lava flow in June 2021. These episodes are potentially very dangerous on island volcanoes since a landslide of hot material that turns into a pyroclastic density current and spreads on the sea surface can threaten mariners and coastal communities, as happened at Stromboli on 3 July and 28 August 2019. In addition, on entering the sea, if their volume is large enough, landslides may trigger tsunamis, as occurred at Stromboli on 30 December 2002. In this paper, we present an integration of multidisciplinary monitoring data, including thermal and visible camera images, ground deformation data gathered from GNSS, tilt, strainmeter and GBInSAR, seismicity, SO2 plume and CO2 ground fluxes and thermal data from the ground and satellite imagery, together with petrological analyses of the erupted products compared with samples from previous similar events. We aim at characterizing the preparatory phase of the volcano that began on December 2020 and led to the May–June 2021 eruptive activity, distinguishing this small intrusion of magma from the much greater 2019 eruptive phase, which was fed by gas-rich magma responsible for the paroxysmal explosive and effusive phases of July–August 2019. These complex eruption scenarios have important implications for hazard assessment and the lessons learned at Stromboli volcano may prove useful for other open conduit active basaltic volcanoes.

Satellite thermal remote sensing is widely used to detect and quantify the high-temperature vol-canic features produced during an eruption, e.g.released radiative power. Some space agencies provide Fire Radiative Power (FRP) Products to characterize any thermal anomaly around the world. In particular, Level-2 FRP Products of the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Sea and Land Surface Temperature Radiometer (SLSTR) are freely available online and they allow to monitor high-temperature volcanic features related to the dynamics of volcanic activity. Here, we propose the FastVRP platform developed in Google Colab to process automatically the FRP Products provided by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) space agencies. FastVRP was designed to monitor the volcanic radiative power (VRP) related to eruptive activity of Mt. Etna (Sicily, Italy). We compared the quality of these FRP Products during a number of recent paroxysmal lava fountains occurred at Etna volcano between February and March 2021. We highlighted the advantages and the limits of each sensor in monitor-ing intense volcanic eruptions lasting a few hours. Furthermore, we combine the mid-high spatial/low temporal resolution VIIRS and SLSTR with the low spatial-high temporal resolution SEVIRI (Spinning Enhanced Visible and Infrared Radiometer Imager) to improve estimates of the energies released from each paroxysmal episode. In particular, we propose a fitting approach to enhance the accuracy of SEVIRI low spatial-high temporal resolution measurements exploiting the few acqui-sitions from VIIRS and SLSTR high spatial-low temporal resolution during lava fountain cooling phase. We validated the radiative power values forecasted from VIIRS and SLSTR with the radiative power values retrieved using MODIS (Moderate Resolution Imaging Spectroradiometer) sensor.

Estimating the areal coverage of newly erupted lava is both a crucial component of volcano monitoring and a powerful tool for characterizing lava flow emplacement behavior. Here, it is presented a methodology based on machine learning, developed in the Google Earth Engine platform, and best fitting approach, to map the extent of lava flows solely using freely available and open-source data from space-borne instruments. Radar and optical satellite data are used as input to different machine learning techniques and best fitting models, so that the methodology is able to operate in all weather conditions. The satellite-driven approach has been successfully used for mapping lava flows automatically during the long sequence of summit eruptions occurred at Mt. Etna between December 2020 and October 2021.

Volcanic thermal anomalies are monitored with an increased application of optical satellite sensors to improve the ability to identify renewed volcanic activity. Hotspot detection algorithms adopting a fixed threshold are widely used to detect thermal anomalies with a minimal occurrence of false alerts. However, when used on a global scale, these algorithms miss some subtle thermal anomalies that occur. Analyzing satellite data sources with machine learning (ML) algorithms has been shown to be efficient in extracting volcanic thermal features. Here, a data-driven algorithm is developed in Google Earth Engine (GEE) to map thermal anomalies associated with lava flows that erupted recently at different volcanoes around the world (e.g., Etna, Cumbre Vieja, Geldingadalir, Pacaya, and Stromboli). We used high spatial resolution images acquired by a Sentinel-2 MultiSpectral Instrument (MSI) and a random forest model, which avoids the setting of fixed a priori thresholds. The results indicate that the model achieves better performance than traditional approaches with good generalization capabilities and high sensitivity to less intense volcanic thermal anomalies. We found that this model is sufficiently robust to be successfully used with new eruptive scenes never seen before on a global scale.

Stromboli volcano has a persistent activity that is almost exclusively explosive. Predomi- nated by low intensity events, this activity is occasionally interspersed with more powerful episodes, known as major explosions and paroxysms, which represent the main hazards for the inhabitants of the island. Here, we propose a machine learning approach to distinguish between paroxysms and major explosions by using satellite-derived measurements. We investigated the high energy explosive events occurring in the period January 2018–April 2021. Three distinguishing features are taken into account, namely (i) the temporal variations of surface temperature over the summit area, (ii) the magnitude of the explosive volcanic deposits emplaced during each explosion, and (iii) the height of the volcanic ash plume produced by the explosive events. We use optical satellite imagery to compute the land surface temperature (LST) and the ash plume height (PH). The magnitude of the explosive volcanic deposits (EVD) is estimated by using multi-temporal Synthetic Aperture Radar (SAR) intensity images. Once the input feature vectors were identified, we designed a k-means unsupervised classifier to group the explosive events at Stromboli volcano based on their similarities in two clusters: (1) paroxysms and (2) major explosions. The major explosions are identified by low/medium thermal content, i.e., LSTI around 1.4 ◦C, low plume height, i.e., PH around 420 m, and low production of explosive deposits, i.e., EVD around 2.5. The paroxysms are extreme events mainly characterized by medium/high thermal content, i.e., LSTI around 2.3 ◦C, medium/high plume height, i.e., PH around 3330 m, and high production of explosive deposits, i.e., EVD around 10.17. The centroids with coordinates (PH, EVD, LSTI) are: Cp (3330, 10.7, 2.3) for the paroxysms, and Cme (420, 2.5, 1.4) for the major explosions.

Volcano-related resurfacing processes can be monitored by complementary using radar and optical sensors. Combining both data sources with machine learning (ML) approaches is fundamental to automatically extract volcanorelated features. Here, a generalized ML approach is developed in Google Earth Engine (GEE) to map lava flows in both nearreal time (NRT) and no-time critical (NTC) time scales. A first attempt towards a generalized classification to automatically map new lava flows is proposed.