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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2026 / Cilt: 28 Sayı: 83

Multi-station GPS-TEC Analysis of Pre-seismic Ionospheric Anomalies: The 2023 Kahramanmaras Earthquakes

Sismik Öncesi İyonküre Anomalilerinin Çok İstasyonlu GPS-TEC Analizi: 2023 Kahramanmaraş Depremleri

Seçil Karatay(KASTAMONU ÜNİVERSİTESİ)0000-0002-1942-6728
Feza Arıkan(HACETTEPE ÜNİVERSİTESİ)0000-0002-6481-1385
Pages
284–295
Publication Date
2026-05-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results provide compelling evidence for Lithosphere-Atmosphere-Ionosphere coupling mechanisms and demonstrate potential applications in earthquake monitoring systems.

📌 3 Key Takeaways

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Objective & Hypothesis:This study investigates pre-seismic ionospheric disturbances preceding the February 6, 2023 Kahramanmaraş earthquake sequence (Mw 7.8 and Mw 7.5) using GPS-derived Total Electron Content (TEC) data from 15 TNPGN-Active stations.
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Key Findings & Data:The Ekinözü station (EKZ1), 15.5 km from the Mw 7.5 epicenter, exhibited the most dramatic correlation drop from ~0.95 to ~0.78.
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Conclusion & Impact:Results provide compelling evidence for Lithosphere-Atmosphere-Ionosphere coupling mechanisms and demonstrate potential applications in earthquake monitoring systems.
🔬Design:Araştırma Makalesi
📍Scope:2023 Verileri
⚙️Variables:Deprem Öncesi İyonküre Bozulmaları · YKS-TEİ Analizi · Kahramanmaraş Depremleri

Abstract

This study investigates pre-seismic ionospheric disturbances preceding the February 6, 2023 Kahramanmaraş earthquake sequence (Mw 7.8 and Mw 7.5) using GPS-derived Total Electron Content (TEC) data from 15 TNPGN-Active stations. The IONOLAB-FFT algorithm was applied to analyze frequency and duration characteristics of ionospheric disturbances during the five-day pre-earthquake period. Cross-correlation analysis reveals systematic temporal evolution, with near-epicentral stations showing pronounced variations. The Ekinözü station (EKZ1), 15.5 km from the Mw 7.5 epicenter, exhibited the most dramatic correlation drop from ~0.95 to ~0.78. Experimental Probability Density Function analysis demonstrates progressive spectral evolution from baseline conditions to increasingly anomalous signatures, with broadened frequency spectra at near-epicentral stations indicating multiple disturbance mechanisms. Duration analysis reveals bimodal distributions at stations closest to epicenters, suggesting coexistence of short-duration acoustic-gravity waves and longer-duration electromagnetic disturbances. Significant ionospheric anomalies begin 2-3 days before the earthquake sequence, with most pronounced changes occurring 24-48 hours prior to the main shock. The analysis establishes clear distance-dependent relationships, with optimal detection sensitivity within 50-100 km of seismic sources. Results provide compelling evidence for Lithosphere-Atmosphere-Ionosphere coupling mechanisms and demonstrate potential applications in earthquake monitoring systems.

Özet

Bu çalışmada, 15 TUSAGA-Aktif YKS istasyonundan kestirilen Toplam Elektron İçeriği (TEİ) verileri kullanılarak 6 Şubat 2023 Kahramanmaraş deprem dizisi (Mw 7.8 ve Mw 7.5) öncesindeki deprem öncesi iyonküre bozulmalar araştırılmıştır. IONOLAB-FFT algoritması, beş günlük deprem öncesi dönemde iyonküre bozulmalarının frekans ve süre özelliklerini analiz etmek için uygulanmıştır. Çapraz korelasyon analizi sistematik zamansal evrimi ortaya koyarken, merkez üssüne yakın istasyonlar belirgin değişimler göstermiştir. Mw 7.5 merkez üssünden 15.5 km uzaklıktaki Ekinözü istasyonu (EKZ1), ~0.95'ten ~0.78'e en çarpıcı korelasyon düşüşünü göstermiştir. Deneysel Olasılık Yoğunluk Fonksiyonu analizi, temel koşullardan giderek artan anormal işaretlere doğru ilerleyen spektral evrimi ortaya koyarken, merkez üssüne yakın istasyonlarda genişletilmiş frekans spektrumları çoklu bozulma mekanizmalarına işaret etmektedir. Süre analizi, merkez üslerine en yakın istasyonlarda bimodal dağılımlar ortaya koyarak kısa süreli akustik-yerçekimi dalgalarının ve daha uzun süreli elektromanyetik bozulmaların bir arada var olduğunu ileri sürmektedir. Önemli iyonküre anomalileri, deprem dizisinden 2-3 gün önce başlar ve en belirgin değişimler ana şoktan 24-48 saat önce meydana gelir. Çalışmanın analiz sonuçları, sismik kaynakların 50-100 km yakınında optimum algılama hassasiyetiyle net mesafeye bağlı ilişkiler ortaya koymuştur. Sonuçlar Litosfer-Atmosfer-İyonküre bağlaşım mekanizmaları için önemli bulgular sunmuş ve deprem izleme sistemlerinde potansiyel uygulamaları işaret etmiştir.

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Cite This Article

Karatay, S., Arıkan, F. (2026). Multi-station GPS-TEC Analysis of Pre-seismic Ionospheric Anomalies: The 2023 Kahramanmaras Earthquakes. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 28 Sayı: 83, 284–295. https://doi.org/10.21205/deufmd.2026288314

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References (31)

  1. Hargreaves JK. The Solar-Terrestrial Environment: An Introduction to Geospace - the Science of the Terrestrial Upper Atmosphere, Ionosphere, and Magnetosphere. Cambridge: Cambridge University Press; 1992.
  2. Kelley MC. The Earth's Ionosphere: Plasma Physics and Electrodynamics. London: Academic Press; 2009.
  3. Ratcliffe JA. An Introduction to the Ionosphere and Magnetosphere. Cambridge: Cambridge University Press; 1972.
  4. Davies K. Ionospheric Radio. London: Peter Peregrinus Ltd.; 1990.
  5. Hofmann-Wellenhof B, Lichtenegger H, Collins J. Global Positioning System Theory and Practice. Wien: Springer-Verlag; 2001.
  6. Kunitsyn VE, Tereshchenko ED. Ionospheric Tomography. Heidelberg: Springer-Verlag Berlin; 2003.
  7. Karatay S. Detection of the ionospheric disturbances on GPS-TEC using Differential Rate Of TEC (DROT) algorithm. Advances in Space Research 2020;65(10):2372-2390. doi:10.1016/j.asr.2020.01.042.
  8. Pulinets S, Boyarchuk K. Ionospheric Precursors of Earthquakes. Heidelberg: Springer Berlin; 2005.
  9. Karatay S, Arikan F, Arikan O. Investigation of total electron content variability due to seismic and geomagnetic disturbances in the ionosphere. Radio Science 2010;45(5):1-12. doi:10.1029/2009RS004313.
  10. Karatay S, Gul SE. Prediction of GPS-TEC on Mw > 5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm. IEEE Geoscience and Remote Sensing Letters 2023;20:1-5. doi:10.1109/LGRS.2023.3262028.
  11. Budak C, Karatay S, Erken F, Cinar A. Classification of the Ionospheric Disturbances Caused by Geomagnetic and Seismic Activity with K-Nearest Neighbors Algorithm. Wireless Personal Communications 2024;134:1551-1569. doi:10.1007/s11277-024-10965-z.
  12. Pulinets SA, Khegai VV, Boyarchuk KA, Lomonosov AM. The atmospheric electric field as a source of variability in the ionosphere. Physics-Uspekhi 1998;41(5):515-522. doi:10.1070/PU1998v041n05ABEH000399.
  13. Pulinets SA, Gaivoronska TB, Contreras AL, Ciraolo L. Correlation analysis technique revealing ionospheric precursors of earthquakes. Natural Hazards and Earth System Sciences 2004;4(5/6):697-702. doi:10.5194/nhess-4-697-2004.
  14. Colonna R, Filizzola C, Genzano N, Lisi M, Tramutoli V. Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region. Geosciences 2023;13(5):1-48. doi:10.3390/geosciences13050150.
  15. Liu JY, Chen YI, Jhuang HK, Lin YH. Ionospheric foF2 and TEC Anomalous Days Associated with M >= 5.0 Earthquakes in Taiwan during 1997-1999. Terrestrial Atmospheric and Oceanic Sciences 2004;15(3):371-383. doi:10.3319/TAO.2004.15.3.371(EP).
  16. Liu JY, Chen YI, Chuo YJ, Tsai HF. Variations of ionospheric total electron content during the Chi-Chi Earthquake. Geophysical Research Letters 2001;28(7):1383-1386. doi:10.1029/2000GL012511.
  17. Akhoondzadeh M. Anomalous TEC variations associated with the powerful Tohoku earthquake of 11 March 2011. Natural Hazards and Earth System Sciences 2012;12(5):1453-1462. doi:10.5194/nhess-12-1453-2012.
  18. Davidenko DV, Pulinets SA. 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 2019;59:493-508. doi:10.1134/S001679321904008X.
  19. Eshkuvatov HE, Ahmedov BJ, Tillayev YA, Tariq MA, Shah MA, Liu L. Ionospheric precursors of strong earthquakes observed using six GNSS stations data during continuous five years (2011-2015). Geodesy and Geodynamics 2023;14(1):65-69. doi:10.1016/j.geog.2022.04.002.
  20. Hernandez-Pajares M, Juan JM, Sanz J. Medium-scale traveling ionospheric disturbances affecting GPS measurements: Spatial and temporal analysis. Journal of Geophysical Research: Space Physics 2006;111(A7):1-13. doi:10.1029/2005JA011474.
  21. Davis MJ. On polar substorms as the source of large-scale traveling ionospheric disturbances. Journal of Geophysical Research 1971;76(19):4525-4533. doi:10.1029/JA076i019p04525.
  22. Arikan F, Yarici A. Spectral investigation of traveling ionospheric disturbances: IONOLAB-FFT. Geodesy and Geodynamics 2017;8(5):297-304. doi:10.1016/j.geog.2017.05.002.
  23. Karatay S. Temporal variations of the ionospheric disturbances due to the seasonal variability over Turkey using IONOLAB-FFT algorithm. Geodesy and Geodynamics 2020;11(3):182-191. doi:10.1016/j.geog.2019.12.002.
  24. Karatay S. Estimation of frequency and duration of ionospheric disturbances over Turkey with IONOLAB-FFT algorithm. Journal of Geodesy 2020;94:1-24. doi:10.1007/s00190-020-01416-1.
  25. Arikan F, Erol CB, Arikan O. Regularized estimation of vertical total electron content from Global Positioning System data. Journal of Geophysical Research: Space Physics 2003;108(A12):1-20. doi:10.1029/2002JA009605.
  26. Arikan F, Erol CB, Arikan O. Regularized estimation of VTEC from GPS data for a desired time period. Radio Science 2004;39(6):1-10. doi:10.1029/2004RS003061.
  27. Nayir H, Arikan F, Arikan O, Erol CB. Total Electron Content Estimation with Reg-Est. Journal of Geophysical Research: Space Physics 2007;112(A11):1-11. doi:10.1029/2007JA012459.
  28. Sezen U, Arikan F, Arikan O, Ugurlu O, Sadeghimorad A. Online, automatic, near-real time estimation of GPS-TEC: IONOLAB-TEC. Space Weather 2013;11(5):291-305. doi:10.1002/swe.20054.
  29. USGS. United States Geological Survey. Available from: https://www.usgs.gov/ [Accessed 26 October 2024].
  30. WDC. World Data Center for Geomagnetism, Kyoto. Available from: https://wdc.kugi.kyoto-u.ac.jp/index.html [Accessed 26 October 2024].
  31. Pulinets SA, Kotsarenko AN, Ciraolo L, Pulinets IA. Special case of ionospheric day-to-day variability associated with earthquake preparation. Advances in Space Research 2007;39(5):970-977. doi:10.1016/j.asr.2006.04.032.