Automatic Signal Discrimination Using Machine Learning on the Data From the Central and Eastern European Infrasound Network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Signal Discrimination Using Machine Learning on the Data From the Central and Eastern European Infrasound Network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic Signal Discrimination Using Machine Learning on the Data From the Central and Eastern European Infrasound Network

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Geophysical Research-Atmospheres

ISSN

2169-897X

e-ISSN

2169-8996

Svazek periodika

130

Číslo periodika v rámci svazku

14

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

e2025JD044047

Kód UT WoS článku

001530291400001

EID výsledku v databázi Scopus

2-s2.0-105010886603