Integrated machine learning approach for volcanic cloud tracking: A Case Study of Etna’s Lava Fountains (2020‑2022)

Abstract

Between December 2020 and February 2022, Mt. Etna produced extraordinary lava fountains which developed into eruptive columns rising several kilometers above the vent. It is crucial to monitor the volcanic clouds produced during these eruptions to assess their impact on the environment, human health, and aviation. Geostationary satellite missions provide high‑frequency thermal infrared data, which are crucial for monitoring volcanic clouds during intense explosive eruptions. However, the large volume of satellite data necessitates automatic and accurate processing algorithms, especially when dealing with global‑scale observations every 5 minutes. In this work, a robust machine learning approach is developed to identify and track volcanic clouds using images from the EUMETSAT MSG SEVIRI (Meteosat Second Generation – Spinning Enhanced Visible and InfraRed Imager).
This approach combines two distinct machine learning models: a deep learning (DL) model for volcanic cloud detection and a supervised machine learning (ML) model for identifying its primary components. The DL model segments volcanic clouds in SEVIRI images by analyzing both the spatial and spectral intensity data. The supervised ML model is able to distinguish the main components of a volcanic cloud by classifying the pixels as ash‑rich, SO2‑rich, or characterized by mixed components. Once an accurate mask of the volcanic cloud is obtained, the volcanic plume height is retrieved from satellite observations for further characterization. This integrated ML approach was applied to characterize the volcanic clouds produced during some of the lava fountains occurred at Etna volcano (Italy) between 2020 and 2022.