Characterization of Volcanic Cloud Components Using Machine Learning Techniques and SEVIRI Infrared Images

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.

TiO2 nanotube array sensor for detecting the SF6 decomposition product SO2

The detection of partial discharge through analysis of SF₆ gas components in gas-insulated switchgear, is significant for the diagnosis and assessment of the operating state of power equipment. The present study proposes the use of a TiO₂ nanotube array sensor for detecting the SF₆ decomposition product SO₂, and the application of the anodic oxidation method for the directional growth of highly ordered TiO₂ nanotube arrays. The sensor response of 10–50 ppm SO₂ gas is tested, and the sensitive response mechanism is discussed. The test results show that the TiO₂ nanotube sensor array has good response to SO₂ gas, and by ultraviolet radiation, the sensor can remove attached components very efficiently, shorten recovery time, reduce chemical poisoning, and prolong the life of the components.