Ionospheric echoes detection in digital ionograms using convolutional neural networks

• Autor: De la Jara Sánchez, César
• Formato: tesis de maestría
• Fecha de Publicación: 2019
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/167490
• Enlace del recurso: http://hdl.handle.net/20.500.12404/14984
• Nivel de acceso: acceso abierto
• Materia: Redes neuronales (Computación); Ionosfera; Sistemas de transmisión de datos; https://purl.org/pe-repo/ocde/ford#1.02.00

An ionogram is a graph that shows the distance that a vertically transmitted wave, of a given frequency, travels before returning to the earth. The ionogram is shaped by making a trace of this distance, which is called virtual height, against the frequency of the transmitted wave. Along with the echoes of the ionosphere, ionograms usually contain a large amount of noise of different nature, that must be removed in order to extract useful information. In the present work, we propose to use a convolutional neural network model to improve the quality of the information obtained from digital ionograms, compared to that using image processing and machine learning techniques, in the generation of electronic density profiles. A data set of more than 900,000 ionograms from 5 ionospheric observation stations is available to use.