Description: The remote sensing of volcanic sulphur dioxide (SO2) is important because it is used as a proxy for volcanic ash, which is dangerous to aviation and is generally more difficult to discriminate. This paper presents an Artificial Neural Network (ANN) algorithm that recognizes volcanic SO2 in the atmosphere using hyperspectral remotely sensed data from the IASI instrument aboard the Metop-A satellite. The importance of this approach lies in exploiting all thermal infrared spectral information of IASI and its application to near real-time volcanic monitoring in a fast manner. In this paper, the ANN algorithm is demonstrated on data of the Eyjafjallajökull volcanic eruption (Iceland) during the months of April and May 2010, and on the Grímsvötn eruption occurring during May 2011. The algorithm consists of a two output neural network classifier trained with a time series consisting of some hyperspectral eruption datasets collected during 14 April to 14 May 2010 and a few from 22 to 26 May 2011. The inputs were all channels (441) in the IASI v3 band and the target outputs (truth) were the corresponding retrievals of SO2 amount obtained with an optimal estimation method. The validation results for the Eyjafjallajökull independent data-sets had an overall accuracy of 100% and no commission errors, therefore demonstrating the feasibility of estimating the presence of SO2 using a neural network approach also a in cloudy sky conditions. Although the validation of the neural network classifier on datasets from the Grímsvötn eruption had no commission errors, the overall accuracies were lower due to the presence of omission errors. Statistical analysis revealed that those false negatives lie near the detection threshold for discriminating pixels affected by SO2. This demonstrated that the accuracy in classification is strictly related to the sensitivity of the model. The lower accuracy obtained in detecting SO2 for Grímsvötn validation dates might also be caused by less statistical knowledge of such an eruption during the training phase.

Description: Published

Description: 246-259

Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio

Description: N/A or not JCR

Description: Artificial neural networks (ANNs) are a valuable and well-established inversion technique for the estimation of geophysical parameters from satellite images; once trained, they help generate very fast results. Furthermore, satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by those natural hazards. This paper describes an application of ANNs as an inverse model for the simultaneous estimation of columnar content and height of sulphur dioxide (SO2) plumes from volcanic eruptions using hyperspectral data from remote sensing. In this study two ANNs were implemented in order to emulate a retrieval model and to estimate the SO2 columnar content and plume height. ANNs were trained using all infrared atmospheric sounding interferometer (IASI) channels between 1000–1200 and 1300–1410 cm−1 as inputs, and the corresponding values of SO2 content and height of plume, obtained from the same IASI channels using the SO2 retrieval scheme by Carboni et al., as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajökull volcano (Iceland) for the months of 2010 April and May and for the Grimsvotn eruption during 2011 May. Both neural networks were trained with a time series consisting of 58 hyperspectral eruption images collected between 2010 April 14 and May 14 and 16 images from 2011 May 22 to 26, and were validated on three independent data sets of images of the Eyjafjallajökull eruption, one in April and the other two in May, and on three independent data sets of the Grímsvötn volcanic eruption that occurred in 2011 May. The root mean square error (RMSE) values between neural network outputs and targets were lower than 20 Dobson units (DU) for SO2 total column and 200 millibar (mb) for plume height. The RMSE was lower than the standard deviation of targets for the Grímsvötn eruption. The neural network had a lower retrieval accuracy when the target value was outside the values used during the training phase

Description: Published

Description: 697–709

Description: 3V. Proprietà dei magmi e dei prodotti vulcanici

Description: JCR Journal

Description: Eruptive columnmodels are powerful tools for investigating the transport of volcanic gas and ash, reconstructing past explosive eruptions, and simulating future hazards. However, the evaluation of these models is challenging as it requires independent estimates of themainmodel inputs (e.g.mass eruption rate) and outputs (e.g. column height). There exists no database of independently estimated eruption source parameters (ESPs) that is extensive, standardized, maintained, and consensus-based. This paper introduces the Independent Volcanic Eruption Source Parameter Archive (IVESPA, ivespa.co.uk), a community effort endorsed by the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI) Commission on Tephra HazardModelling.We compiled data for 134 explosive eruptive events, spanning the 1902-2016 period, with independent estimates of: i) total erupted mass of fall deposits; ii) duration; iii) eruption column height; and iv) atmospheric conditions. Crucially, we distinguish plume top versus umbrella spreading height, and the height of ash versus sulphur dioxide injection. All parameter values provided have been vetted independently by at least two experts. Uncertainties are quantified systematically, including flags to describe the degree of interpretation of the literature required for each estimate. IVESPA also includes a range of additional parameters such as total grain size distribution, eruption style, morphology of the plume (weak versus strong), and mass contribution from pyroclastic density currents, where available. We discuss the future developments and potential applications of IVESPA and make recommendations for reporting ESPs to maximize their usability across different applications. IVESPA covers an unprecedented range of ESPs and can therefore be used to evaluate and develop eruptive column models across a wide range of conditions using a standardized dataset.

Description: Published

Description: 107295

Description: 5V. Processi eruttivi e post-eruttivi

Description: JCR Journal

Description: Rapid and simple estimation of the mass eruption rate (MER) from column height is essential for real-time volcanic hazard management and reconstruction of past explosive eruptions. Using 134 eruptive events from the new Independent Volcanic Eruption Source Parameter Archive (IVESPA, v1.0), we explore empirical MER-height relationships for four measures of column height: spreading level, sulfur dioxide height, and top height from direct observations and as reconstructed from deposits. These relationships show significant differences and highlight limitations of empirical models currently used in operational and research applications. The roles of atmospheric stratification, wind, and humidity remain challenging to detect across the wide range of eruptive conditions spanned in IVESPA, ultimately resulting in empirical relationships outperforming analytical models that account for atmospheric conditions. This finding highlights challenges in constraining the MER-height relation using heterogeneous observations and empirical models, which reinforces the need for improved eruption source parameter data sets and physics-based models.

Description: Published

Description: e2022GL102633

Description: OSV2: Complessità dei processi vulcanici: approcci multidisciplinari e multiparametrici

Description: JCR Journal