Vestnik КRAUNC. Fiz.-Mat. nauki. 2024. vol. 49. no. 4. P. 99-111. ISSN 2079-6641

INFORMATION AND COMPUTING TECHNOLOGIES
https://doi.org/10.26117/2079-6641-2024-49-4-99-111
Research Article
Full text in Russian
MSC 94A08

Contents of this issue

Read Russian Version

Algorithm for Extracting an Artificial Whistler Signal in a Spectrogram Using the PyCharm Integrated Application Development Environment

L. S. Marchenko¹²^{\ast}

¹Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 684034, Kamchatka, Elizovsky District, Paratunka, 7 Mirnaya street, Russia
²Scientific Research Geotechnological Centre FEB RAS, 683002, Petropavlovsk-Kamchatsky, 30 Severo-Vostochnoye highway, Russia

Abstract. The paper proposes an algorithm for identifying the trace of an artificial whistling atmospheric signal (whistle) in a spectrogram, implemented in Python in the PyCharm 2024.1 integrated development environment. The algorithm allows you to identify the whistler trace by setting a certain threshold value (filter). The filter takes into account the signal intensity in the spectrum, the standard deviation of values from the mean, and a certain multiplier that allows you to exclude noise and identify only the most significant peaks in the signal. In the algorithm, using a mask based on the filter, it is possible to obtain an array of frequencies for the trace of an artificial whistler. The computer program allows you to save the resulting array in a text file, which can be used for further analysis in various spreadsheet processors, as well as build whistler trace graphs for visual research. The article tested the adequacy of the algorithm using the example of calculating the dispersion coefficient. It was shown that the algorithm gives good results.

Key words: artificial whistler, spectrogram, trace, filter, mask, Python, PyCharm

Received: 25.10.2024; Revised: 13.11.2024; Accepted: 21.11.2024; First online: 28.11.2024

For citation. Marchenko L. S. Algorithm for extracting an artificial whistler signal in a spectrogram using the PyCharm integrated application development environment. Vestnik KRAUNC. Fiz.-mat. nauki. 2024, 49: 4, 99-111. EDN: YLXDQQ. https://doi.org/10.26117/2079-6641-2024-49-4-99-111.

Funding. The work was supported by IKIR FEB RAS State Task (Reg. No. NIOKTR 124012300245-2).

Competing interests. The authors declare that there are no conflicts of interest regarding authorship and publication.

Contributionand Responsibility. Author is solely responsible for providing the final version of the article in print.

^{\ast}Correspondence: E-mail: marchenko@ikir.ru

The content is published under the terms of the Creative Commons Attribution 4.0 International License

© Marchenko L. S., 2024

© Institute of Cosmophysical Research and Radio Wave Propagation, 2024 (original layout, design, compilation)

References

  1. Artekha S. N., Belyan A. V. On the role of electromagnetic phenomena in some atmospheric processes. Nonlinear Processes in Geophysics. 2013. vol. 20. no. 3. P. 293-304. DOI:10.5194/npg-20-293-2013.
  2. Mahmood N., Edminister J.A. Schaum’s Outline of Electromagnetics. 5th ed. New York: McGraw Hill. 2019.
  3. Lichtenberger J., Ferencz C., Bodn´ar L. et al. Automatic whistler detector and analyzer system: Automatic whistler detector. Geophys. Res. 2008. vol. 113.
  4. Koronczay D., Lichtenberger J., Clilverd M. A. et al. The source regions of whistlers. Journal of Geophysical Research: Space-Physics, 2019. Vol. 124, Pp. 5082–5096.
  5.  Li W., Shen X.-C., Menietti J. D. et al. Global distribution of whistler mode waves in Jovian inner magnetosphere. Geophysical Research Letters. 2020. Vol. 47. No. 15. DOI: 10.1029/2020GL088198.
  6. Morris P.J., Bohdan A., Weidl M. S. et al. Pre-acceleration in the electron foreshock. II. oblique whistler waves. The Astrophysical Journal. 2023. vol. 944. No. 1. Id 13. DOI: 10.3847/1538-4357/acaec8.
  7. Sonwalkar V. S., Reddy A. Specularly reflected whistler: A low-latitude channel to couple lightning energy to the magnetosphere. Science Advances. 2024. Vol. 10. No. 33. eado2657. DOI: 10.1126/sciadv.ado2657.
  8. Xiang T., Liu M., He S., Wang X., Zhou C. Automatic segmentation model and parameter extraction algorithm for lightning whistlers. Radio Science. 2024. vol. 59. no. 11. e2024RS007984. DOI: 10.1029/2024RS007984.
  9. Cherneva N.V., Vodinchar G.M., Sivokon V.P. et al. Correlation analysis of fluxes of whistling atmospherics and lightning discharges Vestnik KRAUNC. Fiziko-Matematiсeskie Nauki. 2013. Vol. 7. No. 2. Pp. 59–67. DOI: 10.18454/2079-6641-2013-7-2-59-67. (In Russian)
  10. Sivokon V.P., Bogdanov V.V., Druzhin G.I. et al. Whistler modulation. Geomagnetizm i Aeronomiya. 2014. vol. 54. no. 6. P. 851–851. DOI: 10.7868/S0016794014060182. (In Russian)
  11. Malush E.A. Algorithm for automatic recognition of whistling atmospherics in real time. Vestnik KRAUNC. Fiziko-Matematiсeskie Nauki. 2015. No. 2(11). P. 82–87. DOI: 10.18454/2079-6641-2015-11-2-82-87. (In Russian)
  12. Malkin E.I., Kazakov E.A., Sannikov D.V. et al. Statistical relationship between whistlers and sprites according to AWDANET and WWLLN. Vestnik KRAUNC. Fiziko-Matematiсeskie Nauki. 2022. Vol. 41. No. 4. P. 178–190. (In Russian)
  13. Storey L. R. O. An investigation of whistling atmospherics. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences. 1953. vol. 246. no. 908. P. 113-141.
  14. Gershman B.N., Korobkov Yu.S. On the theory of propagation of whistling atmospherics. Izvestiya Vuzov. Radiofizika. 1958. Vol. 1. No. 2. Pp. 51–58. (In Russian)
  15. Gershman B.N., Ugarov V.A. Propagation and generation of low-frequency electromagnetic waves in the upper atmosphere. Uspekhi Fizicheskikh Nauk. 1960. Vol. 72. No. 2. P. 235–271. (In Russian)
  16. Shagimuratov I. I. Variations of electron concentration in the plasmasphere according to whistling atmospheric data: specialty 01.04.12. Moscow: 1985. 189 p. (In Russian)
  17. Marchenko L.S., Parovik R.I. Modeling artificial whistlers in Pycharm. News of the Kabardino-Balkarian Scientific Center of RAS. 2024. Vol. 26. No. 5. P. 53–63. DOI: 10.35330/1991-6639-2024-26-5-53-63. (In Russian)
  18. Van Horn, B. M., II; Nguyen, Q. Hands-On Application Development with PyCharm: Build Applications like a Pro with the Ultimate Python Development Tool; Packt Publishing Ltd.: Birmingham, UK, 2023.
  19. Talab A. M. A. et al. Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik. 2016. vol. 127. no. 3. P. 1030-1033.
  20. Mathur N., Mathur S., Mathur D. A novel approach to improve sobel edge detector. Procedia Computer Science. 2016. vol. 93. P. 431-438.
  21. Yan X., Li Y. A method of lane edge detection based on Canny algorithm. IEEE. 2017. P. 2120-2124.

Information about the author

Marchenko Ludmila Sergeevna – leading specialist of the scientist Secretariat of the Institute of Cosmophysical Research and Radio Wave Propagation of the Far Eastern Branch of the Russian Academy of Sciences, Paratunka, postgraduate student, Research Geotechnological Center of the Far Eastern Branch of the Russian Academy of Sciences, Petropavlovsk-Kamchatsky, Russia, ORCID 0000-0003-3634-2443.

ReadDownload article