Skip to main content
Log in

Geoinformation Platform for Monitoring Geophysical Fields, Earthquake Prediction, and Studying Seismogenic Processes

  • GEOINFORMATION TECHNOLOGIES
  • Published:
Journal of Communications Technology and Electronics Aims and scope Submit manuscript

Abstract—A platform for monitoring and analysis of the seismogenic processes is described. The platform consists of two separate GISs. The first system, Web GIS Prognosis, downloads and processes data from remote servers and is used in the systematic Web earthquake prediction and preparation of a project for further analysis. The second system, spatiotemporal GIS GeoTime 3, provides opportunities for detailed study of the data prepared in the GIS Prognosis. Examples of the data analysis results on this platform are presented. The potential of systematic observation of the seismological situation in the GIS Prognosis is demonstrated by the example of California. Using the GIS GeoTime, the efficiencies of the methods for estimating the earthquake epicenter density fields when predicting earthquakes in Kamchatka are compared and it is shown that adaptive weight smoothing yields the best result.

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.

REFERENCES

  1. D. Hyndman and D. Hyndman, “Natural hazards and disasters,” Cengage Learning (2016).

    Google Scholar 

  2. G. A. Sobolev, Fundamentals of Earthquake Forecast (Nauka, Moscow, 1993) [in Russian].

    Google Scholar 

  3. G. A. Sobolev and A. V. Ponomarev, Physics of Earthquake and Forerunner (MAIK “Nauka/Interperiodika”, Moscow, 2003) [in Russian].

  4. R. J. Geller et al., “Earthquakes cannot be predicted,” Science 275 (5306), 1616–1616 (1997).

  5. N. V. Koronovskii and A. A. Naimark, “Unpredictability of earthquakes as fundamental consequence of nonlinearity of geodynamic systems,” Vestn. Mosk. Univ., Ser. 4., Geologiya, No. 6, 3–12 (2012).

  6. I. L. Gufel’d, M. I. Matveeva, and O. N. Novoselov, “Why we cannot carry out the forecast strong crust earthquakes,” Geodin. and Tektonofiz. 2, 378–415 (2011).

  7. V. Keilis-Borok and A. A. Soloviev, (Ed.). Nonlinear Dynamics of the Lithosphere and Earthquake Prediction (Springer Science and Business Media, 2002).

    Google Scholar 

  8. V. Kossobokov and P. Shebalin, Earthquake Prediction in Nonlinear Dynamics of the Lithosphere and Earthquake Prediction (Springer, Berlin, 2003).

    Google Scholar 

  9. F. Corbi et al., “Machine learning can predict the timing and size of analog earthquakes,” Geophys. Res. Lett. 46 (3), 1303–1311 (2019).

  10. P. N. Shebalin et al., “Combining earthquake forecasts using differential probability gains,” Earth, Planets and Space 66, 1–4 (2014).

    Article  Google Scholar 

  11. A. Amei, W. Fu, and C. H. Ho, “Time series analysis for predicting the occurrences of large scale earthquakes,” Int. J. Appl. Sci. and Technol. 2 (7) (2012).

  12. W. Marzocchi and J. D. Zechar, “Earthquake forecasting and earthquake prediction: different approaches for obtaining the best model,” Seismolog. Res. Lett. 82 (3), 442–448 (2011).

  13. M. Moustra, M. Avraamides, and C. Christodoulou, “Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals,” Expert Syst. with Appl. 38 (12), 15032–15039 (2011).

    Article  Google Scholar 

  14. D. A. Rhoades, “Mixture models for improved earthquake forecasting with short-to-medium time horizons,” Bull. Seismolog. Soc. Am. 103 (4), 2203–2215 (2013).

    Article  Google Scholar 

  15. R. Kail, E. Burnaev, and A. Zaytsev, “Recurrent convolutional neural networks help to predict location of earthquakes,” IEEE Geosci. and Remote Sens. Lett. 19, 1–5 (2021).

  16. B. Priambodo, W. F. Mahmudy, and M. A. Rahman, “Earthquake magnitude and grid–based location prediction using backpropagation neural network,” Knowledge Eng. Data Sci. 3, 28–39 (2020).

  17. A. Mignan and M. Broccardo, “Neural network applications in earthquake prediction (1994–2019): Metaanalitic and statistical insights on their limitations,” Seismolog. Res. Lett. 91 (4), 2330–2342 (2020).

  18. K. M. Asim et al., “Earthquake prediction model using support vector regressor and hybrid neural networks,” PloS one 13 (7) (2018).

  19. A. Panakkat and H. Adeli, “Neural network models for earthquake magnitude prediction using multiple seismicity indicators,” Int. J. Neural Syst. 17, 13–33 (2007).

  20. V. G. Gitis and A. B. Derendyaev, “Web–Based GIS platform for automatic prediction of earthquakes,” in Comp. Sci. & Its Appl.–ICCSA 2018: Proc. 18th Int. Conf., Melbourne, VIC, Australia, July 2–5, 2018, Part III, (Springer Int. Publishing, Cham, 2018), pp. 268–283.

  21. V. Gitis and A. Derendyaev, “From monitoring of seismic fields to the automatic forecasting of earthquakes,” Int. J. Web Inf. Syst. (2019).

  22. V. G. Gitis, A. B. Derendyaev, and K. N. Petrov, “Approach to systematic prediction of earthquakes,” J. Commun. Technol. Electron. 67, 764–777 (2022).

    Article  Google Scholar 

  23. V. G. Gitis and A. B. Derendyaev, “Machine learning methods for seismic hazards forecast,” Geosciences 9 (7), 308 (2019).

    Article  Google Scholar 

  24. V. Gitis and A. Derendyaev, “The method of the minimum area of alarm for earthquake magnitude prediction,” Frontiers Earth Sci. 11, 585317 (2020).

    Article  Google Scholar 

  25. V. Gitis, A. Derendyaev, and K. Petrov, “Analyzing the performance of GPS data for earthquake prediction,” Remote Sens. 13 (9), 1842 (2021).

    Article  Google Scholar 

  26. V. G. Gitis, A. B. Derendyaev, and K. N. Petrov, “On the applied efficiency of systematic earthquake prediction,” Comp. Sci. and Its Appl. – ICCSA 2022: Proc. Workshops, Malaga, Spain, July 4–7, 2022, Part III,” (Springer Int. Publishing, Cham, 2022), pp. 607–624.

  27. B. Gutenberg, “The energy of earthquakes,” Quart. J. Geolog. Soc. 112 (1–4), 1–14 (1956).

    Article  Google Scholar 

  28. G. A. Sobolev and Yu. S. Tyupkin, “Anomalies in the mode of weak seismicity before strong earthquakes of Kamchatka,” Vulkanolog. and Seismolog., No. 4, 64–74 (1996).

  29. Gitis, V., Rodkin, M., Derendyaev, A., Wu, Y., and Zhao, J., Study of Precursors of Strong Earthquakes Calculated from Space Geodesic Data. Journal of Communications Technology and Electronics 67 (Suppl 1), S185–S194 (2022).

  30. Yu. V. Riznichenko, “About studying of the seismic mode,” Izv. AN SSSR. Ser. Geofiz., No. 9, 1057–1074 (1958).

  31. V. I. Bune, G. P. Gorshkov, V. N. Krestnikov, et al., Seismic Division into Districts of the Territory of the USSR: Methodical Bases and Regional Description of the Map 1978 (Nauka, Moscow, 1980).

    Google Scholar 

  32. J. Polzehl and V. G. Spokoiny, “Adaptive weights smoothing with applications to image restoration,” J. Royal Statist. Soc.: Ser. B (Statistical Methodology) 62 (2), 335–354 (2000).

    Article  MathSciNet  Google Scholar 

  33. J. Polzehl and V. Spokoiny, “Propagation-separation approach for local likelihood estimation,” Probab. Theory and Related Fields 135, 335–362 (2006).

    Article  MathSciNet  Google Scholar 

  34. S. Kullback, Information Theory and Statistics (Courier Corporation, 1997).

    Google Scholar 

  35. V. G. Gitis et al., “Adaptive estimation of seismic parameter fields from earthquake catalogs,” J. Commun. Technol. Electron. 60, 1459–1465 (2015).

    Article  Google Scholar 

  36. V. G. Gitis et al., “Earthquake prediction using the fields estimated by an adaptive algorithm,” in Proc. 7th Int. Conf. Web Intelligence, Mining and Semantics (WIMS 2017), Amantea, Italy, June 19–22, 2017 (WIMS, 2017), pp. 1–8.

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. G. Gitis.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Translated by E. Bondareva

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gitis, V.G., Derendyaev, A.B., Petrov, K.N. et al. Geoinformation Platform for Monitoring Geophysical Fields, Earthquake Prediction, and Studying Seismogenic Processes. J. Commun. Technol. Electron. 68, 1544–1555 (2023). https://doi.org/10.1134/S1064226923120070

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226923120070

Navigation