Mathematical Geology

, Volume 25, Issue 7, pp 759–772 | Cite as

Statistical analysis and long-term prediction of seismicity for linear zones

  • A. M. Shurygin
Articles

Abstract

The model of the Poisson point process is too vague for earthquake locations in space and time: earthquakes tend to cluster in middle distances and to repulse in large ones. The Poisson point model with variable density makes it possible to describe the tendency for clustering but does not reveal the periodicity of clusters. The author proposes the point-process model where locations of points are determined not by densities of point distribution, but by densities of interpoint differences distribution. In the model, a latent periodicity is revealed and used for prediction of a point process. In 1983, the point-process model prediction was made for the Kuril Islands for 1983–1987 and two signs of danger in time and location were determined. Then they were confirmed by strong earth-quakes. In 1989, a similar prediction was made for North Armenia. The Spitak earthquake in 1988 is clearly seen from the data of previous earthquakes.

Key words

point-process model earthquakes Poisson field Kuril Islands North Armenia 

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References

  1. Cox, D. R., and Lewis, P. A. W., 1966,The Statistical Analysis of Series of Events: John Wiley & Sons, New York, 285 p.Google Scholar
  2. Diggle, P. J., 1976, On Parameter Estimation and Goodness-of-Fit Testing for Spatial Point Patterns: Biometrics, v. 35, p. 87–101.Google Scholar
  3. Fedotov, S. A., and Chernyshov, S. D., 1987, 20 Years of Longtime Seismic Prediction for the Kuril-Kamchatka Arc: Trustworthiness in 1981–1985, 1965–1985 as a Whole and Prediction for 1986–1990: Volcanology and Seismology, no. 6, p. 93–109 (in Russian).Google Scholar
  4. Odinets, M. G., 1983, Statistical Analysis of Earthquakes for the Far East and Middle Asia: Physics Earth, v. 8, p. 20–29 (in Russian).Google Scholar
  5. Ogata, Y., and Tanemura, H., 1984, Likelihood Analysis of Spatial Point Patterns: J. Roy. Stat. Soc., v. 846, p. 496–518.Google Scholar
  6. Shurygin, A. M., 1981, Interpoint Distances and Differences in Characteristics of Point Flow: Theory Prob. Appl., v. 1, p. 196–197.Google Scholar
  7. Shurygin, A. M., 1993, Point Flow with Stationary Distances and Differences: Accepted for Proceedings of the U.S./Japan Conference on Statistical Modeling, 24–29 May 1992, Knoxville, TN.Google Scholar
  8. Shurygin, A. M., and Odinets, M. G., 1984, Longtime Statistical Prediction of Space-Time Density of Strong Earthquakes for the Kuril Islands: Volcanology and Seismology, v. 6, p. 92–102 (in Russian).Google Scholar
  9. Vere-Jones, D., 1978, Earthquake Prediction—A Statistician's View: J. Phys. Earth, v. 26, n. 4, p. 129–146.Google Scholar

Copyright information

© International Association for Mathematical Geology 1993

Authors and Affiliations

  • A. M. Shurygin
    • 1
  1. 1.Department of Mechanics and MathematicsMoscow State UniversityMoscowRussia

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