Skip to main content
Log in

Classification of the Ionospheric Disturbances Caused by Geomagnetic and Seismic Activity with K-Nearest Neighbors Algorithm

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Detection of earthquake-precursor signals a few days before the earthquake day has become an area of increasing interest. In recent years, it has been observed that the major earthquakes and geomagnetic activity can cause significant disturbances and anomalies in the ionospheric parameters such as Total Electron Content (TEC). TEC provides important information about the detection of anomalies and disturbances related to seismic and geomagnetic activity in the ionosphere. The main goal of this study is to classify the disturbances due to the seismic and geomagnetic activity in the ionosphere using TEC data. For this purpose, the K-Nearest Neighbors (K-NN) algorithm is applied to TEC estimated from Global Positioning System stations during five earthquakes with magnitudes Mw greater than 5.6 between 1999 and 2016 and for the geomagnetically quiet and disturbed conditions of the ionosphere. The data is divided into four classes as non-earthquake and non-geomagnetic activity, geomagnetic activity, possible earthquake precursor and earthquake for each earthquake. The study is performed into two groups as Group I, where five days before the earthquake day are marked as precursor, and Group II, where three days are marked as precursors. It is observed that 5055 test samples out of a total of 5184 are classified as true whereas 129 are classified as false for Group I. 3912 are classified as true and 105 are classified as false for Group II. For the possible earthquake class, the Accuracy values increase inversely with the distance of the stations from the epicenter and directly related to the magnitude of the earthquakes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The corresponding author may provide the data and material used in the manuscript subjected to reasonable request.

References

  1. Santis, A. D., Marchetti, D., Pavón-Carrasco, F. J., et al. (2019). Precursory worldwide signatures of earthquake occurrences on Swarm satellite data. Nature Scientific Reports, 9(20287), 1–13.

    Google Scholar 

  2. Akyol, A., Arikan, O., & Arikan, F. (2020). A machine learning-based detection of earthquake precursors using ionospheric data. Radio Science, 55(11), 1–21.

    Google Scholar 

  3. Liu, J., Wang, W., Zhang, X., Wang, Z., & Zhou, C. (2022). Ionospheric total electron content anomaly possibly associated with the April 4, 2010 Mw7.2 Baja California earthquake. Advances in Space Research, 69(5), 2126–2141.

    Google Scholar 

  4. Akhoondzadeh, M., Santis, A. D., Marchetti, D., & Wang, T. (2022). Developing a deep learning-based detector of magnetic, Ne, Te and TEC anomalies from swarm satellites: The case of Mw 7.1 2021 Japan Earthquake. Remote Sensing, 14(7), 1–22.

    Google Scholar 

  5. Petrescu, L., & Moldovan, I. A. (2022). Prospective neural network model for seismic precursory signal detection in geomagnetic field records. Machine Learning and Knowledge Extraction, 4(4), 912–923.

    Google Scholar 

  6. Gurbuz, G., Aktug, B., Jin, S., & Kutoglu, S. H. (2020). A GNSS-based near real time automatic Earth Crust and Atmosphere Monitoring Service for Turkey. Advances in Space Research, 66(12), 2854–2864.

    Google Scholar 

  7. Pulinets, S. A. (2004). Ionospheric precursors of earthquakes: Recent advances in theory and practical applications. Terrestrial Atmospheric and Oceanic Sciences, 15(3), 413–435.

    Google Scholar 

  8. Pulinets, S. A., Gaivoronska, T. B., Contreras, A. L., & Ciraolo, L. (2004). Correlation analysis technique revealing ionospheric precursors of earthquakes. Natural Hazards and Earth System Sciences, 4, 697–702.

    Google Scholar 

  9. Xiong, P., Long, C., Zhou, H., Zhang, X., & Shen, X. (2022). GNSS TEC-based earthquake ionospheric perturbation detection using a novel deep learning framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4248–4263.

    Google Scholar 

  10. Rishbeth, H., & Garriott, O. K. (1969). Introduction to ionospheric physics. Academic Press.

    Google Scholar 

  11. Karatay, S. (2020). Detection of the ionospheric disturbances on GPS-TEC using differential rate Of TEC (DROT) algorithm. Advances in Space Research, 65(10), 2372–2390.

    Google Scholar 

  12. Karatay, S. (2020). Estimation of frequency and duration of ionospheric disturbances over Turkey with IONOLAB-FFT algorithm. Journal of Geodesy, 94(89), 1–24.

    Google Scholar 

  13. Erken, F., Karatay, S., & Cinar, A. (2019). Spatio-temporal prediction of ionospheric total electron content using an adaptive data fusion technique. Geomagnetism and Aeronomy, 59, 971–979.

    Google Scholar 

  14. Arikan, F., Erol, C., & Arikan, O. (2003). Regularized estimation of vertical total electron content from Global Positioning System data. Space Physics, 108(A12), 1–20.

    Google Scholar 

  15. Sezen, U., Arikan, F., Arikan, O., Ugurlu, O., & Sadeghimorad, A. (2013). Online, automatic, near-real time estimation of GPS-TEC: IONOLAB-TEC. Space Weather, 11(5), 297–305.

    Google Scholar 

  16. Laštovička, J. (1996). Effects of geomagnetic storms in the lower ionosphere, middle atmosphere and troposphere. Journal of Atmospheric and Terrestrial Physics, 58(7), 831–843.

    Google Scholar 

  17. Chen, Y., Liu, L., Le, H., Zhang, H., & Zhang, R. (2022). Responding trends of ionospheric F2-layer to weaker geomagnetic activities. Journal of Space Weather and Space Climate, 12(6), 1–12.

    Google Scholar 

  18. Li, H., Wang, J.-S., Chen, Z., Xie, L., Li, F., & Zheng, T. (2020). The contribution of geomagnetic activity to ionospheric foF2 trends at different phases of the solar cycle by SWM. Atmosphere, 11(6), 1–12.

    Google Scholar 

  19. Pulinets, S., & Ouzounov, D. (2011). Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model—An unified concept for earthquake precursors validation. Journal of Asian Earth Sciences, 41(4–5), 371–382.

    Google Scholar 

  20. Carbone, V., Piersanti, M., Materassi, M., Battiston, R., Lepreti, F., & Ubertini, P. (2021). A mathematical model of lithosphere–atmosphere coupling for seismic events. Nature Scientific Reports, 11, 1–12.

    Google Scholar 

  21. Bolt, B. (1964). Seismic air waves from the great 1964 Alaskan earthquake. Nature, 202, 1095–1096.

    Google Scholar 

  22. Donn, W. L., & Posmentier, E. S. (1964). Ground-coupled air waves from the Great Alaskan Earthquake. Journal of Geophysical Research, 69(24), 5357–5361.

    Google Scholar 

  23. Davies, K., & Baker, D. M. (1965). Ionospheric effects observed around the time of the Alaskan earthquake of March 28, 1964. Journal of Geophysical Research, 70(9), 2251–2253.

    Google Scholar 

  24. Leonard, R. S., & Barnes Jr, R. A. (1965). Observation of ionospheric disturbances following the Alaska earthquake. Journal of Geophysical Research, 70(5), 1250–1253.

    Google Scholar 

  25. Row, R. V. (1966). Evidence of long-period acoustic-gravity waves launched into the F region by the Alaskan earthquake of March 28, 1964. Journal of Geophysical Research, 71(1), 343–345.

    Google Scholar 

  26. Hirshberg, J., Currie, R. G., & Breiner, S. (1967). Long period geomagnetic fluctuations after the 1964 Alaskan earthquake. Earth and Planetary Science Letters, 3, 426–428.

    Google Scholar 

  27. Yuen, P. C., Weaver, P. F., Suzuki, R. K., & Furumoto, A. S. (1969). Continuous, traveling coupling between seismic waves and the ionosphere evident in May 1968 Japan earthquake data. Journal of Geophysical Research, 74(9), 2256–2264.

    Google Scholar 

  28. Weaver, P. F., Yuen, P. C., Prolss, G. W., & Furumoto, A. S. (1970). Acoustic coupling into the ionosphere from seismic waves of the earthquake at Kurile Islands on August 11, 1969. Nature, 226, 1239–1241.

    Google Scholar 

  29. Antsilevich, M. G. (1971). The influence of Tashkent earthquake on the earth's magnetic field and the ionosphere, Tashkent earthquake 26 April 1966. In FAN Publishing House, pp. 187–188.

  30. Datchenko, E., Ulomov, V., & Chernyshova, C. (1973). Electron density anomalies as the possible precursor of Tashkent earthquake. Academy of Sciences, 12, 30–32.

    Google Scholar 

  31. Larkina, V. I., Migulin, V. V., Molchanov, O. A., Kharkov, I. P., Inchin, A. S., & Schvetcova, V. B. (1989). Some statistical results on very low frequency radiowave emissions in the upper ionosphere over earthquake zones. Physics of the Earth and Planetary Interiors, 57(1–2), 100–109.

    Google Scholar 

  32. Liu, J. Y., Chen, Y. I., Pulinets, S. A., Tsai, Y. B., & Chuo, Y. J. (2000). Seismo-ionospheric signatures prior to M≥6.0 Taiwan earthquakes. Geophysical Research Letters, 27(19), 3113–3116.

    Google Scholar 

  33. Pulinets, S. A., Contreras, A. L., Bisiacchi-Giraldi, G., & Ciraolo, L. (2005). Total eletron content variations in the ionosphere before the Colima, Mexico, earthquake of 21 January 2003. Geofísica Internacional, 44(4), 369–377.

    Google Scholar 

  34. Pulinets, S., Kotsarenko, A., Ciraolo, L., & Pulinets, I. A. (2007). Special case of ionospheric day-to-day variability associated with earthquake preparation. Advances in Space Research, 39(5), 970–977.

    Google Scholar 

  35. Karatay, S., Arikan, F., & Arikan, O. (2010). Investigation of total electron content variability due to seismic and geomagnetic disturbances in the ionosphere. Radio Science, 4(5), 1–12.

    Google Scholar 

  36. Pulinets, S. A., Khegal, V. V., Boyarchuk, K. A., & Lomonosov, A. M. (1998). The atmospheric electric field as a source of variability in the ionosphere. Physics-Uspekhi, 41(5), 515–522.

    Google Scholar 

  37. Liu, J. Y., et al. (2004). Ionospheric foF2 and TEC anomalous days associated with M >= 5.0 earthquakes in Taiwan during 1997–1999. Terrestrial Atmospheric and Oceanic Sciences, 15(3), 371–383.

    Google Scholar 

  38. Liu, J. Y., Chen, Y. I., Chen, C. H., & Hattori, K. (2010). Temporal and spatial precursors in the ionospheric global positioning system (GPS) total electron content observed before the 26 December 2004 M9.3 Sumatra-Andaman Earthquake. Journal of Geophysical Research Space Physics, 115(A9), 1–13.

    Google Scholar 

  39. Kouris, S., Polimeris, K., & Cander, L. R. (2006). Specifications of TEC variability. Advances in Space Research, 37(5), 983–1004.

    Google Scholar 

  40. Akhoondzadeh, M. (2016). Decision Tree, Bagging and Random Forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (Mw = 8.8) earthquake of 27 February 2010. Advances in Space Research, 57(12), 2464–2469.

    Google Scholar 

  41. Davidenko, D. V., & Pulinets, S. A. (2019). Deterministic variability of the ionosphere on the eve of strong (M ≥ 6) earthquakes in the regions of Greece and Italy according to long-term measurements data. Geomagnetism and Aeronomy, 59, 493–508.

    Google Scholar 

  42. Budak, C., Turk, M., & Toprak, A. (2016). Removal of impulse noise in digital images with na"ıv al of impulse noise in digital images with naiıve Bayes. Turkish Journal of Electrical Engineering and Computer Sciences, 24(4), 2717–2729.

    Google Scholar 

  43. Albayrak, A. (2022). Classification of analyzable metaphase images using transfer learning and fine tuning. Medical & Biological Engineering & Computing, 60, 239–248.

    Google Scholar 

  44. Sarea, A. M., Subramanian, S., & Alareeni, B. (2021). Web-based financial disclosures by using machine learning analysis: Evidence from Bahrain. In The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success, (pp. 357–371) Springer.

  45. Kim J. H., Kim B. S., & Savarese S. (2012). Comparing image classification methods: K-nearest-neighbor and support-vector-machines. In 6th WSEAS international conference on Computer Engineering and Applications, Harvard Cambridge.

  46. Budak, C., & Mencik, A. (2022). Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method. Neural Computing and Applications, 34, 13499–13512.

    Google Scholar 

  47. Guzella, T. S., & Caminhas, W. M. (2009). A review of machine learning approaches to Spam filtering. Expert Systems with Applications, 36(7), 10206–10222.

    Google Scholar 

  48. USGS, United States Geological Survey Earthquake Hazards Program. Available: https://earthquake.usgs.gov/.

  49. Arikan, F., Erol, C., & Arikan, O. (2004). Regularized estimation of vertical total electron content from GPS data for a desired time period. Radio Science, 39(6), 1–10.

    Google Scholar 

  50. Nayir, H., Arikan, F., Arikan, O., & Erol, C. (2007). Total electron content estimation with reg-est. Journal of Geophysical Research Space Physics, 112(A11), 1–11.

    Google Scholar 

  51. Arikan, F., Nayir, H., Sezen, U., & Arikan, O. (2008). Estimation of single station interfrequency receiver bias using GPS-TEC. Radio Science, 43(4), 1–13.

    Google Scholar 

  52. Arikan F., Sezen U., Toker C., Artuner H., Bulu G., Demir U., Erdem E., Arikan O., Tuna H., Gulyaeva T. L., Karatay S., & Mosna Z., (2016). Space weather studies of IONOLAB group. In URSI Asia-Pacific Radio Science Conference (URSI AP-RASC), Seul.

  53. Bureau I. C. International GNSS Service, NASA Jet Propulsion Laboratory California Institute of Technology. Available: https://igs.org/network/. Retrieved 2022.

  54. NOAA, National Oceanic and Atmospheric Administration. Available: ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/.

  55. WDC, World Data Center for Geomagnetism, Kyoto, [Online]. https://wdc.kugi.kyoto-u.ac.jp/. Retrieved 20 Feb 2023.

  56. Tasci E., & Onan, A. (2017) K-En Yakın Komşu Algoritması Parametrelerinin. In Akademik Bilisim Conference, Aydin Turkey.

  57. Raschka, S. (1969). Python machine learning. Packt Publishing.

    Google Scholar 

  58. Hu, L.-Y., Huang, M.-W., Ke, S.-W., & Tsai, C.-F. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, cilt 5, no. 1304, pp. 1–9.

  59. Taunk, K., De, S., Verma, S., & Swetapadma, A. (2019). A brief review of nearest neighbor algorithm for learning and classification. In International conference on ıntelligent computing and control systems (ICCS), Madurai India.

Download references

Acknowledgements

The authors wish to thank Prof. Dr. Feza Arikan and IONOLAB group for their outstanding efforts on IONOLAB-BIAS and IONOLAB-TEC Algorithm.

Funding

The authors declare that no funds, grants, or other supports were received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Contributions

CB, SK, Faruk Erken and Ali Cinar made all calculations, created the algorithm and wrote the main manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Secil Karatay.

Ethics declarations

Conflict of interest

The authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Approval

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Budak, C., Karatay, S., Erken, F. et al. Classification of the Ionospheric Disturbances Caused by Geomagnetic and Seismic Activity with K-Nearest Neighbors Algorithm. Wireless Pers Commun 134, 1551–1569 (2024). https://doi.org/10.1007/s11277-024-10965-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-10965-z

Keywords

Navigation