Japan Geoscience Union Meeting 2019

Presentation information

[J] Poster

P (Space and Planetary Sciences ) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM16] Physics and Chemistry in the Atmosphere and Ionosphere

Thu. May 30, 2019 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Yuichi Otsuka(Institute for Space-Earth Environmental Research, Nagoya University), Takuya Tsugawa(National Institute of Information and Communications Technology), Seiji Kawamura(National Institute of Information and Communications Technology)

[PEM16-P11] Detection of spread-F echoes in ionograms using machine learning

*Hiroyuki Nakata1, Yuta Goto2, Hiroyo Ohya1, Toshiaki Takano1 (1.Graduate School of Engineering, Chiba University, 2.Department of Electrical and Electronics Engineering)

Keywords:spread F, ionogram, machine learning

Ionospheric irregularities referred to as equatorial spread-F is very important phenomena in terms of radio wave propagation because their spatial scales are from centimeters to tens of kilometer and they affects wide-band radio waves. Therefore, they influence the reliability of satellite-ground communications, navigation systems and so on. The ionogram is one of the important observation system of spread-F. Because the detection of spread-F in the ionogram is done manually, detection of spread-F could be replaced with automatic detection by developing an excellent machine learning system. In this study, therefore, we have developed a method to detect spread-F in the ionogram automatically using machine learning.
In this detection process, we have arranged two classifiers for typical frequency-type and mixed (frequency-type and range-type) spread-F echoes in the ionograms. A number of correct images must be prepared for these classifiers to learn the feature values of spread-F. To do so, ten thousands of correct images were generated by create_samples utility provided by OpenCV library. Using these classifiers, we have detected the both types of spread-F echos in the ionograms observed at 4 observatories (Wakkanai, Kokubunji, Yamagawa, Ogimi) utilized by National Institute of Information and Communications Technology. The detection rates of both types of spread-F echos are from 50% to 73% and both classifier detected both types of spread-F echos similarly. Namely, the classifier for the frequency-type spread-F echo detected both of the frequency-type and mixed-type echos, and vice versa. In case where the two types of spread-F echos are not distinguished, on the other hand, both of classifiers detect 80% of total spread-F echos. In addition, the classifiers also picked up some other phenomenon , such as overlapped traces of O-mode and X-mode, multi-hop trace.
As for the future plan, there might be several way to improve the detection rate; to use more classifiers, to make the classifiers be precisely with more training data, and so on. However, a number of correct images are inevitable in machine learning. In order to prepare such a number of images, it may be effective to generate training data using deep generative model.