Advertisement

Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 1172–1185 | Cite as

Systematization of Ionospheric, Geodynamic, and Thermal Precursors of Strong (M ≥ 6) Earthquakes Detected from Space

  • V. G. BondurEmail author
  • M. N. Tsidilina
  • E. V. Gaponova
  • O. S. Voronova
USE OF SPACE INFORMATION ABOUT THE EARTH

Abstract

The article describes the formation features of short- and mid-term strong (M ≥ 6) earthquake precursors detected from space. A database has been created that contains parameter variations for the ionospheric, geodynamic, and thermal fields detected from space during the preparation and occurrence of significant seismic events. This database is an important element of a unified integrated system for monitoring catastrophic natural disasters. We present a technique for the collection and systematization of satellite and related information, as well as data processing methods to obtain information on irregular variations of various geophysical fields in seismic regions. This database contains general information on more than 4000 earthquakes, as well as the characteristics of 80 strong earthquakes (magnitudes from 6.0 to 9.1) that occurred in various world regions between 1990 and 2017. We also present examples from the database of visualization and analysis of data for various precursors of an earthquake that occurred in Italy on August 24, 2016 (M = 6.2).

Keywords:

earthquakes earthquake precursors remote sensing database natural disasters 

Notes

ACKNOWLEDGMENTS

The study was supported by a grant from the Russian Science Foundation (project no. 16-17-00139) at the AEROCOSMOS Research Institute.

REFERENCES

  1. 1.
    Akopyan, S.Ts., Bondur, V.G., and Rogozhin, E.A., Technology for monitoring and forecasting strong earthquakes in Russia with the use of the seismic entropy method, Izv., Phys. Solid Earth, 2017, vol. 53, no. 1, pp. 32–51.CrossRefGoogle Scholar
  2. 2.
    Andrianov, V.A. and Smirnov, V.M., Determination of the height profile of electron concentration of the ionosphere by dual-frequency measurements of satellite signals, Radiotekh. Elektron., 1993, vol. 38, no. 7, pp. 1326–1335.Google Scholar
  3. 3.
    Baklanov, A.A., Bondur, V.G., Klaić, Z.B., and Zilitinkevich, S.S., Integration of geospheres in Earth systems: Modern queries to environmental physics, modelling, monitoring and education, Geofizika, 2012, no. 29, pp. 1–4.Google Scholar
  4. 4.
    Bondur, V.G., Principles of construction of the space system for monitoring of the Earth for environmental and natural-resource purposes, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 1995, no. 2, pp. 14–38.Google Scholar
  5. 5.
    Bondur, V.G., Aerospace methods and technologies for monitoring oil and gas areas and facilities, Izv., Atmos. Ocean. Phys., 2011, vol. 47, no. 9, pp. 1007–1018.CrossRefGoogle Scholar
  6. 6.
    Bondur, V.G., Modern approaches to processing large hyperspectral and multispectral aerospace data flows, Izv., Atmos. Ocean. Phys., 2014, vol. 50, no. 9, 840–852.CrossRefGoogle Scholar
  7. 7.
    Bondur, V.G. and Kuznetsova, L.V., Satellite monitoring of seismic hazard area geodynamics using the method of lineament analysis, Proceedings of the 31st International Symposium on Remote Sensing of Environment (ISRSE), 2005, pp. 376–379.Google Scholar
  8. 8.
    Bondur, V.G. and Savin, A.I., Conception on forming systems for the remote monitoring of the environment for the purpose of ecologic and natural resources, Issled. Zemli Kosmosa, 1992, no. 6, pp. 70–78.Google Scholar
  9. 9.
    Bondur, V.G. and Smirnov, V.M., Method for monitoring seismically hazardous territories by ionospheric variations recorded by satellite navigation systems, Dokl. Earth Sci., 2005a, vol. 403, no. 5, pp. 736–740.Google Scholar
  10. 10.
    Bondur, V.G. and Smirnov, V.M., Monitoring of ionosphere variations during the preparation and realization of earthquakes using satellite navigation system data, in Proceedings of the 31st International Symposium on Remote Sensing of Environment (ISRSE), 2005b, pp. 372–375.Google Scholar
  11. 11.
    Bondur, V.G. and Starchenkov, S.A., Methods and programs for processing and classification of aerospace images, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2001, no. 3, pp. 118–143.Google Scholar
  12. 12.
    Bondur, V.G. and Voronova, O.S., Variations in outgoing longwave radiation during the preparation and realization of strong earthquakes on the territory of Russia in 2008 and 2009, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2012, no. 1, pp. 79–85.Google Scholar
  13. 13.
    Bondur, V.G. and Zverev, A.T., A method of earthquake forecast based on the lineament dynamic analysis using satellite imagery, Issled. Zemli Kosmosa, 2005a, no. 3, pp. 37–52.Google Scholar
  14. 14.
    Bondur, V.G. and Zverev, A.T., A method of earthquake forecast based on the lineament analysis of satellite images, Doklady Earth Sci., 2005b, vol. 402, no. 4, pp. 561–567.Google Scholar
  15. 15.
    Bondur, V.G. and Zverev, A.T., Lineament system formation mechanisms registered in space images during the monitoring of seismic danger areas, Issled. Zemli Kosmosa, 2007, no. 1, pp. 47–56.Google Scholar
  16. 16.
    Bondur, V.G., Garagash, I.A., Gokhberg, M.B., Lapshin, V.M., Nechaev, Yu.V., Steblov, G.M., and Shalimov, S.L., Geomechanical models and ionospheric variations related to strongest earthquakes and weak influence of atmospheric pressure gradients, Dokl. Earth Sci., 2007, vol. 414, no. 1, pp. 666–669.CrossRefGoogle Scholar
  17. 17.
    Bondur, V.G., Krapivin, V.F., and Savinykh, V.P., Monitoring i prognozirovanie prirodnykh katastrof (Monitoring and Forecasting of the Natural Disasters), Moscow: Nauchnyi mir, 2009.Google Scholar
  18. 18.
    Bondur, V.G., Garagash, I.A., Gokhberg, M.B., Lapshin, V.M., Nechaev, Yu.V., Connection between variations of the stress-strain state of the Earth’s crust and seismic activity: The example of Southern California, Dokl. Earth Sci., 2010, vol. 430, no. 2, pp. 147–150.CrossRefGoogle Scholar
  19. 19.
    Bondur, V.G., Zverev, A.T., Gaponova, E.V., and Zima A.L. Space methods in predictive cyclic dynamics of lineament system before preparation of the earthquakes, Issled. Zemli Kosmosa, 2012, no. 1, pp. 3–20.Google Scholar
  20. 20.
    Bondur, V.G., Garagash, I.A, Gokhberg, M.B., and Rodkin, M.V., The Evolution of the Stress State in Southern California Based on the Geomechanical Model and Current Seismicity, Izv., Phys. Solid Earth, 2016, vol. 52, no. 1, pp. 117–128.Google Scholar
  21. 21.
    Bondur, V.G., Tsidilina, M.N., Gaponova, E.V., and Voronova, O.S., Joint analysis of various precursors of seismic events using remote sensing data at the example of earthquake in Italy (24.08.2016, M6.2), in 17th International Multidisciplinary Science GeoConference (SGEM), 29 June–5 July, 2017, Albena, Bulgaria, 2017, pp. 149–162.Google Scholar
  22. 22.
    Davis, C.A., Keilis-Borok, V., Molchan, G., Shebalin, P., Lahr, P., and Plumb, C., Earthquake prediction and disaster preparedness, Nat. Hazards Rev., 2010, vol. 11, no. 4, pp. 173–184.CrossRefGoogle Scholar
  23. 23.
    Dey, S. and Singh, R.P., Surface latent heat flux as an earthquake precursor, Nat. Hazards Earth Syst. Sci., 2003, vol. 3, pp. 749–755.CrossRefGoogle Scholar
  24. 24.
    Fedotov, S.A., Dolgosrochnyi seismicheskii prognoz dlya Kurilo-Kamchatskoi dugi (Long-Term Seismic Forecast for the Kuril–Kamchatka Arc), Moscow: Nauka, 2005.Google Scholar
  25. 25.
    Gvishiani, A.D., Dzeboev, B.A., and Agayan, S.M., A new approach to recognition of the strong earthquake-prone areas in the Caucasus, Izv., Phys. Solid Earth, 2013, vol. 49, no. 6, pp. 747–766.CrossRefGoogle Scholar
  26. 26.
    Harrison, R.G., Aplin, K.L., and Rycroft, M.J., Atmospheric electricity coupling between earthquake regions and the ionosphere, J. Atmos. Sol.-Terr. Phys., 2010, vol. 72, nos. 5–6, pp. 376–381. http://10.1016/ j.jastp.2009.12.004CrossRefGoogle Scholar
  27. 27.
    http://geoenv.ruGoogle Scholar
  28. 28.
    Keilis-Borok, V., Gabrielov, A., and Soloviev, A., Geo-complexity and earthquake prediction, in Encyclopedia of Complexity and Systems Science, Meyers, R., Ed., New York: Springer, 2009, pp. 4178–4194.Google Scholar
  29. 29.
    Liu, J.Y., Le, H., Chen, Y.I., Chen, C.H., Liu, L., Wan, W., Su, Y.Z., Sun, Y.Y., Lin, C.H., and Chen, M.Q., Observations and simulations of seismoionospheric GPS total electron content anomalies before the 12 January 2010 M7 Haiti earthquake, J. Geophys. Res., 2011, vol. 116, no. A4.Google Scholar
  30. 30.
    Lyubushin, A.A., Analysis of coherence in global seismic noise for 1997–2012, Izv., Phys. Solid Earth, 2014, vol. 50, no. 3, pp. 325–333.CrossRefGoogle Scholar
  31. 31.
    Prirodnye opasnosti Rossii (Natural Hazards in Russia), vol. 2: Seismicheskie opasnosti (Seismic Hazards), Sobolev, G.A., Ed., Moscow: KRUK, 2000.Google Scholar
  32. 32.
    Prirodnye opasnosti Rossii. Seismicheskie opasnosti (Natural Hazards in Russia. Seismic Hazards), Osipov, V.I. and Shoigu, S.K., Eds., Moscow, 2001.Google Scholar
  33. 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.Google Scholar
  34. 34.
    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. 540–558.Google Scholar
  35. 35.
    Savin, A.I. and Bondur, V.G., Scientific fundamentals of creation and diversification of global aerospace systems, Opt. Atmos. Okeana, 2000, vol. 13, no. 1, pp. 38–53.Google Scholar
  36. 36.
    Sobolev, G.A. and Ponomarev, A.V., Fizika zemletryasenii i predvestniki (Physics of Earthquakes and Precursors), Moscow: Nauka, 2003.Google Scholar
  37. 37.
    Sorokin, V.M. and Hayakawa, M., Generation of seismic-related dc electric fields and lithosphere–atmosphere–ionosphere coupling, Mod. Appl. Sci., 2013, vol. 7, no. 6, pp. 1–25.CrossRefGoogle Scholar
  38. 38.
    Tikhonov, A.N., Goncharskii, A.V, Stepanov, V.V., et al., Regulyariziruyushchie algoritmy i apriornaya informatsiya (Regularization Algorithms and A Priori Information), Moscow: Nauka, 1983.Google Scholar
  39. 39.
    Tronin, A.A., Satellite remote sensing in seismology. A review, Remote Sens., 2010, vol. 2, no. 1, pp. 124–150.CrossRefGoogle Scholar
  40. 40.
    Zav’yalov, A.D., Srednesrochnyi prognoz zemletryasenii. Osnovy, metodika, realizatsiya (Midterm Prediction of Earthquakes. Fundamentals, Methods, and Implementation), Moscow: Nauka, 2006.Google Scholar
  41. 41.
    Zotov, O.D., Guglielmi, A.V., and Sobisevich, A.L., On magnetic precursors of earthquakes, Izv., Phys. Solid Earth, 2013, vol. 49, no. 6, pp. 882–889.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • V. G. Bondur
    • 1
    Email author
  • M. N. Tsidilina
    • 1
  • E. V. Gaponova
    • 1
  • O. S. Voronova
    • 1
  1. 1.AEROCOSMOS Research Institute for Aerospace MonitoringMoscowRussia

Personalised recommendations