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 to 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. On a test set of 2050 ionograms the
ResNet50 model provides an accuracy of 89 percent, recall of 87 percent,
precision of 95 percent as well as Area Under the Curve (AUC) of 96
percent. In addition to the model, this work also provides a labelled
dataset of over 28,000 ionograms, which is extremely useful for the
community for future machine learning studies.
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