Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164181
Title: Automatic spread-F detection using deep learning
Authors: Luwanga, Christopher
Fang, Tzu-Wei
Chandran, Amal
Lee, Yu-Ju
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Luwanga, C., Fang, T., Chandran, A. & Lee, Y. (2022). Automatic spread-F detection using deep learning. Radio Science, 57(5), e2021RS007419-. https://dx.doi.org/10.1029/2021RS007419
Journal: Radio Science
Abstract: Spread-F (SF) is a feature that can be visually observed on ionograms when the ionosonde signals are significantly impacted by plasma irregularities in the ionosphere. Depending on the scale of the plasma irregularities, radio waves of different frequencies are impacted differently when the signals pass through the ionosphere. An automated method for detecting SF in ionograms is presented in this study. Through detecting the existence of SF in ionograms, we can help identify instances of plasma irregularities that are potentially affecting the high-frequency radio-wave systems. The ionogram images from Jicamarca observatory in Peru, during the years 2008–2019, are used in this study. Three machine learning approaches have been carried out: supervised learning using Support Vector Machines, and two neural network-based learning methods: autoencoder and transfer learning. Of these three methods, the transfer learning approach, which uses convolutional neural network architectures, demonstrates the best performance. The best existing architecture that is suitable for this problem appears to be the ResNet50. With respect to the training epoch number, the ResNet50 showed the greatest change in the metric values for the key metrics that we were tracking. Furthermore, on a test set of 2050 ionograms, the model based on the ResNet50 architecture provides an accuracy of 89%, recall of 87%, precision of 95%, as well as Area Under the Curve of 96%. The work also provides a labeled data set of around 28,000 ionograms, which is extremely useful for the community for future machine learning studies.
URI: https://hdl.handle.net/10356/164181
ISSN: 0048-6604
DOI: 10.1029/2021RS007419
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 American Geophysical Union. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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