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<p>A seismic regime model for background earthquakes in the Ossetian sector of the Greater Caucasus has been developed. The model parameters were calculated via the high-contrast “mean-position method”, where computed values are assigned to the mean-position of earthquake epicenters in the sample rather than to the center of the counting circle. The calculations employed the most complete and representative integrated earthquake catalog for the region, with a unified magnitude scale. The results from the quantitative verification (including the <i>L</i>-test) and reconstruction of the regional magnitude–frequency distributions of background earthquakes demonstrate good agreement between the model data and the observed seismic data used to construct the model. The spatial correlation between the epicenters of strong earthquakes and areas with high local event recurrence rates for a&#xa0;given magnitude in the model supports its reliability.</p>

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Parameters of the Seismic Regime of the Ossetian Sector of the Greater Caucasus

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • I. A. Vorobieva,
  • B. A. Dzeboev,
  • A. D. Gvishiani,
  • B. V. Dzeranov,
  • E. O. Kedrov,
  • P. A. Malyutin,
  • Y. V. Barykina

摘要

A seismic regime model for background earthquakes in the Ossetian sector of the Greater Caucasus has been developed. The model parameters were calculated via the high-contrast “mean-position method”, where computed values are assigned to the mean-position of earthquake epicenters in the sample rather than to the center of the counting circle. The calculations employed the most complete and representative integrated earthquake catalog for the region, with a unified magnitude scale. The results from the quantitative verification (including the L-test) and reconstruction of the regional magnitude–frequency distributions of background earthquakes demonstrate good agreement between the model data and the observed seismic data used to construct the model. The spatial correlation between the epicenters of strong earthquakes and areas with high local event recurrence rates for a given magnitude in the model supports its reliability.