Observación Geofísica, Desarrollo e Innovación Tecnológica
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Browsing Observación Geofísica, Desarrollo e Innovación Tecnológica by Author "De la Jara, César"
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Item Restricted Ionospheric echo detection in digital ionograms using convolutional neural networks(American Geophysical Union, 2021-08) De la Jara, César; Olivares, C.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.Item Open Access Ionospheric echoes detection in digital ionograms using convolutional neural networks(Pontificia Universidad Católica del Perú, 2019) De la Jara, César; Olivares Poggi, César AugustoAn 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.