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Method for Cognitive Identification of Ionospheric Precursors of Earthquakes

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Abstract

A significant proportion of publications related to the ionospheric disturbances that arise during earthquake preparation over the regions of their preparation refer to these disturbances as anomalies. In this case, the identification of the ionospheric precursor is actually based on an estimate of the amplitude of the deviation of the ionospheric parameters from the undisturbed value. We propose a completely different approach based on the physical mechanism of the generation of disturbances created by the interaction of the ionosphere with the lithosphere and atmosphere. At the same time, this interaction gives the observed variations unique properties that are typical only for earthquake precursors, based on which the precursors are identified with an intelligent algorithm. Another advantage of this approach is that the method, which we call “cognitive identification”, does not require large deviations from unperturbed values, since it is based on recognition of the “image” of the precursor. It is created in a way that considers morphological features of the precursors and can be effectively used even at low values of the signal/noise ratio.

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REFERENCES

  1. Afraimovich, E.L., Astafyeva, E.I., Oinats, A.V., Yasuke-vich, Y.V., and Zhivetiev, I.V., Global electron content: a new conception to track solar activity, Ann. Geophys., 2008, vol. 26, pp. 335–344.

    Article  Google Scholar 

  2. Alipour, A., Yarahmadi, J., and Mahdavi, M., Comparative study of M5 model tree and artificial neural network in estimating reference evapotranspiration using MODIS products, J. Climatol., 2014, id 839205. https://doi.org/10.1155/2014/839205

  3. Arikan, O. and Arikan, F., Machine learning based detection of earthquake precursors using ionospheric data, in 42nd COSPAR Scientific Assembly, Pasadena, Calif., 2018, id C1.4-16-18.

  4. Bošková, J., Šmilauer, J., Jiříček, F., and Tříska, P., Is the ion composition of outer ionosphere related to seismic activity, J. Atmos. Sol.-Terr. Phys., 1993, vol. 55, no. 13, pp. 1689–1695.

    Article  Google Scholar 

  5. Cushman-Roisin, B., Atmospheric boundary layer, in Environmental Fluid Mechanics, New York: John Wiley and Sons, 2014, pp. 165–186.

    Google Scholar 

  6. Das, V., Pollack, A., Wollner, U., and Mukerji, T., Convolutional neural network for seismic impedance inversion, Geophysics, 2019, vol. 84, no. 6. https://doi.org/10.1190/geo2018-0838.1

  7. Davidenko, D.V. and Pulinets, S.A., 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, Geomagn. Aeron. (Engl. Transl.), 2019, vol. 59, no. 4, pp. 493–507. https://doi.org/10.1134/S001679321904008X

  8. Davies, K. and Baker, D.M., Ionospheric effects observed around the time of the Alaskan earthquake of March 28, 1964, J. Geophys. Res., 1965, vol. 70, pp. 2251–2253.

    Article  Google Scholar 

  9. Dobrovolsky, I.P., Zubkov, S.I., and Myachkin, V.I., Estimation of the size of earthquake preparation zones, Pure Appl. Geophys., 1979, vol. 117, no. 5, pp. 1025–1044.

    Article  Google Scholar 

  10. Gol’din, S.V., Physics of the “living” Earth, in Problemy geofiziki XXI v (Problems of Geophysics in the XXI Century), Moscow: Nauka, 2003, pp. 17–36.

  11. Hernández-Pajares, M., Juan, J.M., Sanz, J., et al., The ionosphere: Effects, GPS modeling and the benefits for space geodetic techniques, J. Geod., 2011, vol. 85, pp. 887–907. https://doi.org/10.1007/s00190-011-0508-5

    Article  Google Scholar 

  12. Kelley, M.C. and Heelis, R.A., The Earth’s Ionosphere. Plasma Physics and Electrodynamics, San Diego: Academic, 1989.

    Google Scholar 

  13. Kim, Y. and Nakata, N., Geophysical inversion versus machine learning in inverse problems, The Leading Edge, 2018, vol. 37, pp. 866–944.

    Article  Google Scholar 

  14. Kon, S., Nishihashi, M., and Hattori, K., Ionospheric anomalies possibly associated with M ≥ 6.0 earthquakes in the Japan area during 1998–2010: Case studies and statistical study, J. Asian Earth Sci., 2011, vol. 41, pp. 410–420.

    Article  Google Scholar 

  15. Kunitsyn, V.E., Andreeva, E.S., Kozharin, M.A., and Nesterov, I.A., Ionosphere radio tomography using high-orbit navigation systems, Mos. Univ. Phys. Bull., 2005, vol. 60, no. 1, pp. 94–108.

    Google Scholar 

  16. Le, H., Liu, J.Y., and Liu, L., A statistical analysis of ionospheric anomalies before 736 M6.0+ earthquakes during 2002–2010, J. Geophys. Res., 2011, vol. 116, A02303. https://doi.org/10.1029/2010JA015781

    Article  Google Scholar 

  17. Liu, J.Y., Chen, Y.I., Jhuang, H.K., and Lin, Y.H., Ionospheric foF2 and TEC anomalous days associated with M ≥ 5.0 earthquakes in Taiwan during 1997–1999, Terr. Atmos. Ocean. Sci., 2004, vol. 15, no. 3, pp. 371–383.

    Article  Google Scholar 

  18. Liu, J.Y., Chen, Y.I., Chuo, Y.J., and Chen, C.S., A statistical investigation of preearthquake ionospheric anomaly, J. Geophys. Res., 2006, vol. 111, A05304. https://doi.org/10.1029/2005JA011333

    Article  Google Scholar 

  19. Liu, J.Y., Chen, Y.I., Chen, C.H., et al., Seismoionospheric GPS total electron content anomalies observed before the 12 May 2008 Mw7.9 Wenchuan earthquake, J. Geophys. Res., 2009, vol. 114, A04320. https://doi.org/10.1029/2008JA013698

    Article  Google Scholar 

  20. Nikolaev, A.V., Features of geophysics in the XXI century, in Problemy geofiziki XXI v (Problems of Geophysics in the XXI Century), Moscow: Nauka, 2003, pp. 7–16.

  21. Ouzounov, D., Pulinets, S., Liu, J.Y., et al., Multiparameter assessment of pre-earthquake atmospheric signals, in Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies, Ouzounov, D., Pulinets, S., Hattori, K., and Taylor, P., Eds., AGU/Wiley, 2018a, pp. 339–359.

    Book  Google Scholar 

  22. Ouzounov, D., Pulinets, S., Kafatos, M., and Taylor, P., Thermal radiation anomalies associated with major earthquakes, in Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies, Ouzounov, D., Pulinets, S., Hattori, K., and Taylor, P., Eds., AGU/Wiley, 2018b, pp. 259–274.

    Book  Google Scholar 

  23. Pulinets, S.A., Seismic activity as a source of the ionospheric variability, Adv. Space Res., 1998, vol. 22, no. 6, pp. 903–906. https://doi.org/10.1016/S0273-1177(98)00121-5

    Article  Google Scholar 

  24. Pulinets, S. and Davidenko, D., Ionospheric precursors of earthquakes and Global Electric Circuit, Adv. Space Res., 2014, vol. 53, no. 5, pp. 709–723. https://doi.org/10.1016/j.asr.2013.12.035

    Article  Google Scholar 

  25. Pulinets, S.A. and Davidenko, D.V., The nocturnal positive ionospheric anomaly of electron density as a short-term earthquake precursor and the possible physical mechanism of its formation, Geomagn. Aeron. (Engl. Transl.), 2018, vol. 58, no. 4, pp. 559–570. https://doi.org/10.1134/S0016793218040126

  26. Pulinets, S.A. and Legen’ka, A.D., Dynamics of the near-equatorial ionosphere prior to strong earthquakes, Geomagn. Aeron. (Engl. Transl.), 2002, vol. 42, no. 2, pp. 227–232.

  27. Pulinets, S.A. and Legen’ka, A.D., Spatial–Temporal characteristics of large scale disturbances of electron density observed in the ionospheric F-region before strong earthquakes, Cosmic Res., 2003, vol. 41, no. 3, pp. 221–229.

    Article  Google Scholar 

  28. Pulinets, S. and Ouzounov, D., The Possibility of Earthquake Forecasting: Learning from Nature, Bristol, UK: IOP Publishing, 2018.

    Book  Google Scholar 

  29. Pulinets, S.A., Legen’ka, A.D., and Zelenova, T.I., Local-time dependence of seismo-ionospheric variations at the F-layer maximum, Geomagn. Aeron. (Engl. Transl.), 1998, vol. 38, no. 3, pp. 400–402.

  30. Pulinets, S.A., Legen’ka, A.D., Gaivoronskaya, T.V., and Depuev, V.Kh., Main phenomenological features of ionospheric precursors of strong earthquakes, J. Atmos. Sol. Terr. Phys., 2003, vol. 65, pp. 1337–1347.

    Article  Google Scholar 

  31. Pulinets, S.A., Gaivoronska, T.B., Leyva Contreras, A., and Ciraolo, L., Correlation analysis technique revealing ionospheric precursors of earthquakes, Nat. Hazard. Earth Syst., 2004, vol. 4, pp. 697–702.

    Article  Google Scholar 

  32. Pulinets, S.A., Kotsarenko, A.N., Ciraolo, L., and Pulinets, I.A., Special case of ionospheric day-to-day variability associated with earthquake preparation, Adv. Space Res., 2007, vol. 39, no. 5, pp. 970–977.

    Article  Google Scholar 

  33. Pulinets, S.A., Bondur, V.G., Tsidilina, M.N., and Gaponova, M.V., Verification of the concept of seismoionospheric coupling under quiet heliogeomagnetic conditions, using the Wenchuan (China) earthquake of May 12, 2008, as an example, Geomagn. Aeron. (Engl. Transl.), 2010, vol. 50, no. 2, pp. 231–242.

  34. Pulinets, S.A., Ouzounov, D.P., and Davidenko, D.V., Is the forecast of earthquakes possible?, in Integral’nye tekhnologii mnogoparametricheskogo monitoringa geoeffektivnykh yavlenii v ramkakh kompleksnoi modeli vzaimosvyazei v litosfere, atmosfere i ionosfere Zemli (Integral Technologies of Multiparametric Monitoring of Geoeffective Phenomena in the Framework of a Complex Model of Interrelations in the Earth’s Lithosphere, Atmosphere, and Ionosphere), Moscow: Trovant, 2014.

    Google Scholar 

  35. Pulinets, S.A., Ouzounov, D.P., Karelin, A.V., and Davidenko, D.V., Physical bases of the generation of short-term earthquake precursors: A complex model of ionization-induced geophysical processes in the lithosphere–atmosphere–ionosphere–magnetosphere system, Geomagn. Aeron. (Engl. Transl.), 2015, vol. 55, no. 4, pp. 521–538.

  36. Pulinets, S., Ouzounov, D., Karelin, A., and Davidenko, D., Lithosphere–atmosphere–ionosphere–magnetosphere coupling—a concept for pre-earthquake signals generation, in Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies, Ouzounov, D., Pulinets, S., Hattori, K., and Taylor, P., Eds., AGU/Wiley, 2018, pp. 79–98.

    Google Scholar 

  37. Ryu, K., Parrot, M., Kim, S.G., Jeong, K.S., Chae, J.S., Pulinets, S., and Oyama, K.-I., Suspected seismo-ionospheric coupling observed by satellite measurements and GPS TEC related to the M7.9 Wenchuan earthquake of 12 May 2008, J. Geophys. Res.: Space Phys., 2014, vol. 11, pp. 10305–10323.

    Google Scholar 

  38. Sun, J., Slang, S., Elboth, T., Larsen Greiner, T., McDonald, S., and Gelius, L.-J., A convolutional neural network approach to deblending seismic data, Geophysics, 2020, vol. 85, no. 4. https://doi.org/10.1190/geo2019-0173.1

  39. Zolotov, O.V., Earthquake effects in variations of the total electron content of the ionosphere, Extended Abstract of Cand. Sci. (Phys.–Math.) Diss., Murmansk State Technological University, Murmansk, 2015.

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Authors and Affiliations

  1. Space Research Institute, Russian Academy of Sciences (IKI RAS), Moscow, Russia

    S. A. Pulinets & D. V. Davidenko

  2. Korolev Rocket and Space Public Corporation Energia, Korolev, (RSC Energia), Korolev, Moscow oblast, Russia

    D. V. Davidenko

  3. Fedorov Institute of Applied Geophysics Roshydromet (IAG), Moscow, Russia

    P. A. Budnikov

Authors
  1. S. A. Pulinets
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  2. D. V. Davidenko
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  3. P. A. Budnikov
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Correspondence to S. A. Pulinets, D. V. Davidenko or P. A. Budnikov.

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This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation in accordance with the Agreement on the Grant of Subsidy no. 075-11-2019-015 dated October 22, 2019. Unique project identifier RFMEFI58519X0008.

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Pulinets, S.A., Davidenko, D.V. & Budnikov, P.A. Method for Cognitive Identification of Ionospheric Precursors of Earthquakes. Geomagn. Aeron. 61, 14–24 (2021). https://doi.org/10.1134/S0016793221010126

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  • DOI: https://doi.org/10.1134/S0016793221010126

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