Abstract

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 and interference 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 extract ionospheric layers profiles, improving 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.