2020/02/04 by Othniel J. E. Y. Konan, Konan, Othniel J. E. Y., Amit Kumar Mishra +3 · 1 citation
Computer Science · Earth and Planetary Sciences · Engineering · Mathematics · Physics and Astronomy · #Acoustics #Antenna (radio) #Artificial intelligence #Computer science #Earthquake Detection and Analysis #Geology #Geophysics #Image (mathematics) #Ionosphere and magnetosphere dynamics #Lightning (connector) #Lightning and Electromagnetic Phenomena #Magnetic field #Magnetosphere #Noise (video) #Physics #Plasmasphere #Power (physics) #Remote sensing #Seismic Waves and Analysis #Spectrogram #Telecommunications #Very low frequency #Whistler #cs.LG #eess.SP #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.01244
published in arXiv (Cornell University) (Cornell University) · 20 pages, 13 tables, 26 figures, Preliminary work presented at the Machine Learning in Heliophysics hosted in September 2019 in Amsterdam (https://ml-helio.github.io/). Code can be found at (https://github.com/Kojey/MSc-whistler-waves-detector)
arxiv created 2020/02/04 · openalex publication_date 2020/02/04 · arxiv updated 2020/02/05 · openalex created_date 2022/07/26 · openalex updated_date 2026/08/08
Lightning strokes create powerful electromagnetic pulses that routinely cause\nvery low frequency (VLF) waves to propagate across hemispheres along\ngeomagnetic field lines. VLF antenna receivers can be used to detect these\nwhistler waves generated by these lightning strokes. The particular\ntime/frequency dependence of the received whistler wave enables the estimation\nof electron density in the plasmasphere region of the magnetosphere. Therefore\nthe identification and characterisation of whistlers are important tasks to\nmonitor the plasmasphere in real-time and to build large databases of events to\nbe used for statistical studies. The current state of the art in detecting\nwhistler is the Automatic Whistler Detection (AWD) method developed by\nLichtenberger (2009). This method is based on image correlation in 2 dimensions\nand requires significant computing hardware situated at the VLF receiver\nantennas (e.g. in Antarctica). The aim of this work is to develop a machine\nlearning-based model capable of automatically detecting whistlers in the data\nprovided by the VLF receivers. The approach is to use a combination of image\nclassification and localisation on the spectrogram data generated by the VLF\nreceivers to identify and localise each whistler. The data at hand has around\n2300 events identified by AWD at SANAE and Marion and will be used as training,\nvalidation, and testing data. Three detector designs have been proposed. The\nfirst one using a similar method to AWD, the second using image classification\non regions of interest extracted from a spectrogram, and the last one using\nYOLO, the current state of the art in object detection. It has been shown that\nthese detectors can achieve a misdetection and false alarm of less than 15% on\nMarion's dataset.\n