Using deep learning for studying spread-F in the ionosphere

• Main Author: Luwanga, Christopher
• Other Authors: Erry Gunawan
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/172317
• Institution: Nanyang Technological University

In this work we have developed an automated method for detecting Spread-F in ionograms based on machine learning (ML) algorithms. Spread-F is a feature that appears on an ionogram when there are specific ionospheric irregularities at the time the ionogram is taken. Our work contributes in three key ways: 1) help other researchers quickly decide on the most suitable Spread F detection methodology, 2) provide publicly available labelled ionogram dataset for others to use to build their own SF classification models and 3) develop an interactive web application through which one can view ionograms, check whether a given ionogram has SF, as well as vote on already classified ionograms. The methods explored in this work include Support Vector Machines and convolutional neural networks (CNN). For CNN, we created models based on existing public neural network architectures in a process called transfer learning. Transfer learning outperformed the other approaches and the best performing architecture was the one based on ResNet50.