Analysis of the Ionospheric Total Electron Content during the Series of September 2017 Solar Flares over the Philippine - Taiwan Region

Mendoza, Merlin M.; Macalalad, Ernest P.; Juadines, Kyle Ezekiel S.

doi:10.1109/iconspace.2019.8905982

Cited by 3 publications

(1 citation statement)

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“…The low-latitude ionosphere is highly variable due to the daily, seasonal, and solar cycle variations, as well as due to the impacts of the external and internal drivers. These external drivers are related to space weather effects such as solar flares [ 1 ], geomagnetic storms [ 2 ], and particle precipitations from the radiation belts [ 3 ]. The internal sources are related to tidal effects [ 4 ], earthquakes [ 5 ], and tsunamis [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Recovery of Ionospheric Signals Using Fully Convolutional DenseNet and Its Challenges

Mendoza

Дмитриев

et al. 2021

Sensors

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The technique of active ionospheric sounding by ionosondes requires sophisticated methods for the recovery of experimental data on ionograms. In this work, we applied an advanced algorithm of deep learning for the identification and classification of signals from different ionospheric layers. We collected a dataset of 6131 manually labeled ionograms acquired from low-latitude ionosondes in Taiwan. In the ionograms, we distinguished 11 different classes of the signals according to their ionospheric layers. We developed an artificial neural network, FC-DenseNet24, based on the FC-DenseNet convolutional neural network. We also developed a double-filtering algorithm to reduce incorrectly classified signals. That made it possible to successfully recover the sporadic E layer and the F2 layer from highly noise-contaminated ionograms whose mean signal-to-noise ratio was low, SNR = 1.43. The Intersection over Union (IoU) of the recovery of these two signal classes was greater than 0.6, which was higher than the previous models reported. We also identified three factors that can lower the recovery accuracy: (1) smaller statistics of samples; (2) mixing and overlapping of different signals; (3) the compact shape of signals.

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