Automatic Signal Discrimination Using Machine Learning on the Data From the Central and Eastern European Infrasound Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378289%3A_____%2F25%3A00637687" target="_blank" >RIV/68378289:_____/25:00637687 - isvavai.cz</a>
Result on the web
<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JD044047" target="_blank" >https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JD044047</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1029/2025JD044047" target="_blank" >10.1029/2025JD044047</a>
Alternative languages
Result language
angličtina
Original language name
Automatic Signal Discrimination Using Machine Learning on the Data From the Central and Eastern European Infrasound Network
Original language description
A labeled data set of 216,681 infrasound detections was compiled using data from the Central and Eastern European Infrasound Network (CEEIN). Detections associated with quarry blasts, thunderstorms, eruptions of the Etna volcano, industrial activity, and the war in Ukraine were categorized using ground truth information, such as seismic and lightning data. To establish benchmark performance, a random forest classifier and a convolutional neural network (CNN) were trained separately, achieving F1 scores of 0.8170 and 0.8248 on the test set, respectively. An ensemble model, combining both classifiers, outperformed them achieving an F1 score of 0.8773. The model, initially trained on four CEEIN arrays, was tested on data from a separate station not included in training. Although performance initially declined, transfer learning and fine-tuning of the CNN and retraining the random forest model improved the ensemble model's F1 score to 0.9056 making it a considerable step. These results represent significant progress in automatic infrasound signal classification for monitoring the atmosphere.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10509 - Meteorology and atmospheric sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Geophysical Research-Atmospheres
ISSN
2169-897X
e-ISSN
2169-8996
Volume of the periodical
130
Issue of the periodical within the volume
14
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
e2025JD044047
UT code for WoS article
001530291400001
EID of the result in the Scopus database
2-s2.0-105010886603