Many natural and man-made phenomena cause ionospheric responses that can be captured in total electron content data obtained from Global Navigation Satellite System observations. However, the increasing volume of the data poses challenges for its analysis. This study provides an algorithm for detecting covolcanic ionospheric disturbances in total electron content time series based on machine learning. Using the Sarychev Peak eruption (June 11-16, 2009) as an example, we identified and labeled the observational data, proposed data features, and generated datasets. The design and number of features were chosen considering the computational efficiency of the algorithm and its potential applicability in monitoring systems. A machine learning model based on a gradient boosting technique was trained and achieved a Matthews correlation coefficient of 0.86 on the test dataset, indicating the high quality. The proposed algorithm detects 96 % of the disturbances in the datasets with a minimal number of false positives (48 out of 200 test files), exhibiting a significant improvement compared with the conventional STA\LTA algorithm, which detected only 11 % of the disturbances. The validation of the algorithm on data corresponding to the Calbuco (2015) and Hunga Tonga-Hunga Ha'apai (2022) volcanoes eruptions showed 94 % and 81 % of the disturbances detected, respectively. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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