EGU22-5143, updated on 27 Mar 2022
https://doi.org/10.5194/egusphere-egu22-5143
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Identification and separation of infrasound signals from storms and quarry blasts via machine learning algorithms

Marcell Pásztor1,2 and István Bondár2
Marcell Pásztor and István Bondár
  • 1ELTE Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Geophysics and Space Science, Budapest, Hungary (pasztorms@gmail.com)
  • 2Institue for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences, Budapest, Hungary (ibondar2014@gmail.com)

The infrasound array in Hungary has been operational since May 2017 at Piszkés-tető. Since then, it has collected over a million PMCC detections from various known sources such as microbaroms from the Northern Atlantic, quarry blasts and mine explosions, eruptions of Etna, storms, airplanes and so on. The goal of this study is to train, test, validate and compare machine learning models such as Random Forest and Support Vector Machine, for identification and separation of infrasound signals from storms and quarry blasts. The dataset contains identified signals from previous studies and from the Hungarian Seismo-Acoustic Bulletins. The features for training are extracted both from the time and frequency domains of the signals.

How to cite: Pásztor, M. and Bondár, I.: Identification and separation of infrasound signals from storms and quarry blasts via machine learning algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5143, https://doi.org/10.5194/egusphere-egu22-5143, 2022.

Comments on the display material

to access the discussion