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Leveraging machine learning techniques and SEVIRI data to detect volcanic clouds composed of ash; ice; and SO<sub>2</sub>
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DOI：10.5194/amt-19-4255-2026.png)
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Abstract
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Abstract. Volcanic clouds can influence the climate and pose a serious threat to air transportation. Detecting and distinguishing them from meteorological clouds is particularly challenging because they often are composed of water vapor and ice particles; along with ash and gases. This study presents a neural network (NN) model for the detection of volcanic clouds composed of ash; ice; and SO2; applied to data acquired by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite instrument. A dataset of 1259 SEVIRI images related to Mount Etna volcano (Italy) eruptions spanning from 2020 to 2022; as well as 2024; was considered. The NN model; based on a multi-layer perceptron (MLP); was developed using 13 features; including thermal infrared channels and brightness temperature differences (BTDs). A post-processing step based on a plume-tracking algorithm and a Non-Local means filter was implemented to improve the performance of the NN model. The model was validated using three eruptive events that were not included in the training phase; achieving an overall balanced accuracy of up to 92.0 %. The validation results also showed that the model successfully detected 66.0 %; 48.5 %; and 84.1 % of the observed volcanic cloud (VC) pixels in the three analysed validation events; respectively. In addition; only 7.7 %; 4.0 %; and 21.9 % of the detected VC pixels corresponded to false alarms for the respective events. Thus; the model demonstrates the capability to detect volcanic clouds even under complex conditions of high meteorological cloud cover. The results are promising for the automatic detection of volcanic clouds; including those containing ice and SO2; as well as for improving volcanic cloud retrieval processes.
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