An ionogram is a graph of the time that a vertically transmitted wave takes to return to the earth as a function of frequency. Time is typically represented as virtual height, which is the time divided by the speed of light. The ionogram is shaped by making a trace of this 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 a method based on convolutional neural networks to extract ionospheric echoes from digital ionograms. Extraction using the CNN model is compared with extraction using machine learning techniques. From the extracted traces, ionospheric parameters can be determined and electron density profile can be derived.

The initial development of the algorithm was funded by a Wellcome Trust Career Development Fellowship awarded to DAJM [078067/Z/05] and by the Peruvian FINCYT Proyecto de Interes Nacional [PIN 061] and CONCYTEC PROCYT awarded to MZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.