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On Back-Propagation Network to Early Judgment of Seismic Sequences

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

The early predictions of earthquake sequence types are studied using BP Network. Back-Propagation network is a feedforward neural network practiced by back propagation algorithm, is one of neural network modes applied widely. It not only can approximate any continuous function, has the strong nonlinear mapping ability, but also has a strong robustness, memory capacity and self-learning ability. On the basis of Ms ≥ 5.0 earthquake sequence materials in our country since 1970, It is effective for early predictions of earthquake sequence types that we divides the data in 5 time scales according to 1–7 days after the earthquake.

Keywords

Back-Propagation network Seismic sequence Type Early prediction 

Notes

Acknowledgement

This work is supported by projects of National Natural Science Foundation (NNSF) of China under Grant 41474087 and Spark Program of Earthquake Science and Technology (XH16003).

References

  1. 1.
    Liu, Z.R.: A code of practice for earthquake prediction using frequency attenuation of earthquakes. Earthquake 1, 35–37 (1984)Google Scholar
  2. 2.
    Lucile, M.J.: Foreshocks and time-dependent earthquake hazard assessment in southern California. Bull. Seism. Soc. Am. 75(6), 1669–1679 (1985)Google Scholar
  3. 3.
    Annemarie, C., Euan, G.C.: Foreshock rates from aftershock abundance. Bull. Seism. Soc. Am. 98(5), 2133–2148 (2008)CrossRefGoogle Scholar
  4. 4.
    Gentili, S., Bressan, G.: The partitioning of radiated energy and the largest aftershock of seismic sequences occurred in the northeastern Italy and western Slovenia. J. Seismol. 12(3), 343–354 (2008)CrossRefGoogle Scholar
  5. 5.
    Han, W.B., Wang, H., Zeng, J., Xi, D.L.: Study on the early comprehensive determination method of post-earthquake trend of middle strong earthquake. Acta Seismol. Sin. 15(1), 15–21 (1993)Google Scholar
  6. 6.
    Zhou, C.Y., Zhang, Y.X., Wang, H.W.: The comprehensive index of early judgment of seismic sequences extracted by pattern recognition method. Acta Seismol. Sin. 18(1), 118–124 (1996)Google Scholar
  7. 7.
    Vorobieva, I.A.: Prediction of a subsequent large earthquake. Phys. Earth Planet. Inter. 111, 197–206 (1999)CrossRefGoogle Scholar
  8. 8.
    Jiang, H.K., Fu, Z.X., Liu, J., Lu, P.L.: Research on earthquake sequence in mainland China. Seismological Press, Beijing (2007)Google Scholar
  9. 9.
    Wu, K.T., Jiao, Y.B., Lu, P.L., Wang, Z.D.: Introduction to Earthquake Sequence. Peking University Press, Beijing (1990)Google Scholar
  10. 10.
    Wang, X.C., Shi, F., Yu, L., Li, Y.: Analysis of 43 cases of MATLAB neural network. Beijing University Press of Aeronautics and Astronautics, Beijing (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Beijing Earthquake AgencyBeijingChina

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