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Forecasting aftershock activity: 2. Estimating the area prone to strong aftershocks

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Abstract

The technique for forecasting the spatial domain where fairly intense aftershocks should be expected after a strong earthquake is considered. The paper presents the task of estimating the area prone to the strong future aftershocks using the data for the first 12 h after the main shock. The existing aftershock identification techniques are inapplicable to this task because they either analyze the distributions of the epicenters of the aftershock process that has been already completed or only consider the parameters of the main shock and only provide rough estimates. Using the developed criteria of estimating the quality of the prediction, we quantitatively compared quite a few different candidates. The latter included the main known techniques and their modifications suggested by us. In these modifications, we took into account the results of the recent studies on the dynamics of the aftershock process. This enabled us to select the optimal procedure which demonstrated the best results of the quantitative tests for more than 120 aftershock sequences with the magnitudes starting from 6.5 all over the world. This procedure can be used in the seismological monitoring centers for forecasting the area prone to the aftershock activity after a strong earthquake based on the data of operative processing.

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References

  • ANSS Catalogue, Northern California Earthquake Data Center, UCBerkeley Seismological Laboratory. http://earthquake.usgs.gov/data/. Cited February 11, 2016. doi 10.7932/NCEDC10.7932/NCEDC

  • Baranov, S.V. and Shebalin, P.N, Forecasting aftershock activity: 1. Adaptive estimates based on the Omori and Gutenberg–Richter laws, Izv., Phys. Solid Earth, 2016, vol. 52, no. 3, pp. 413–431. doi 10.7868/S0002333716020034

    Article  Google Scholar 

  • Brodsky, E., Long-range triggered earthquakes that continue after the wave train passes, Geophys. Res. Lett., 2006, vol. 33, L15313. doi 10.1029/2006GL026605

  • Cattania, C., Hainzl, S., Wang, L., Enescu, B., and Roth, F, Aftershock triggering by postseismic stresses: a study based on Coulomb rate-and-state models, J. Geophys. Res., 2015, vol. 120, no. 4, pp. 2388–2407. doi 10.1002/2014JB011500

    Article  Google Scholar 

  • Cisternas, A., Godefroy, P., Gvishiani, A., Gorshkov, A.I., Kosobokov, V., Lambert, M., Ranzman, E., Sallantin, J., Saldano, H., Soloviev, A., and Weber, C., A dual approach to recognition of earthquake prone areas in the Western Alps, Ann. Geophys., 1985, vol. 3, no. 2, pp. 249–270.

    Google Scholar 

  • Cisternas, A., Philip, H., Bousquet, J.C., Cara, M., Deschamps, A., Dorbath, L., Dorbath, C., Haessler, H., Jimenez, E., Nercessian, A., Rivera, L., Romanowicz, B., Gvishiani, A., Shebalin, N.V., Aptekman, I., et al., The Spitak (Armenia) earthquake of 7 December 1988: field observations, seismology and tectonics, Nature, 1989, vol. 339, no. 6227, pp. 675–679.

    Google Scholar 

  • Davis, C., Keilis-Borok, V., Molchan, G., Shebalin, P., Lahr, P., and Plumb, C, Earthquake prediction and disaster preparedness: interactive analysis, Nat. Hazards Rev., 2010, vol. 11, no. 4, pp. 173–184.

    Article  Google Scholar 

  • Gardner, J.K. and Knopoff, L, Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian?, Bull. Seismol. Soc. Am., 1974, vol. 64, no. 5, pp. 1363–1367.

    Google Scholar 

  • Knopoff, L, The magnitude distribution of declustered earthquakes in Southern California, Proc. Natl. Acad. Sci., 2000, vol. 97, no. 22, pp. 11880–11884.

    Article  Google Scholar 

  • Kossobokov, V. and Shebalin, P., Earthquake prediction, in Nonlinear Dynamics of the Lithosphere and Earthquake Prediction, Keilis-Borok, V.I. and Soloviev, A.A., Eds., Berlin: Springer, 2003, pp. 141–207.

    Chapter  Google Scholar 

  • Marsan, D., and Lengline, O, Extending earthquakes’ reach through cascading, Science, 2008, vol. 319, pp. 1076–1079.

    Article  Google Scholar 

  • Marsan, D., Helmstetter, A., Bouchon, M., and Dublanchet, P, Foreshock activity related to enhanced aftershock production, Geophys. Res. Lett., 2014, vol. 41, pp. 1–7. doi 10.1002/2014GL061219

    Article  Google Scholar 

  • Mignan, A. and Woessner, J., Estimating the magnitude of completeness for earthquake catalogs, Community Online Resource for Statistical Seismicity Analysis, 2012. http://www.corssa.org. doi 10.5078/corssa-00180805

  • Molchan, G, Structure of optimal strategies in earthquake prediction, Tectonophysics, 1991, vol. 193, pp. 267–276.

    Article  Google Scholar 

  • Molchan, G.M. and Dmitrieva, O.E., Identifying the aftershocks: a review and new approaches, in Vychislitel’naya seismologiya, vyp. 24 (Computational Seismology, vol. 24), Moscow, 1991, pp. 19–50.

    Google Scholar 

  • Molchan, G.M. and Dmitrieva, O.E, Aftershock identification: methods and new approaches, Geophys. J. Int., 1992, vol. 109, pp. 501–516.

    Article  Google Scholar 

  • Molchan, G., Space-time earthquake prediction: the error diagrams, Pure Appl. Geophys., 2010, vol. 167, nos. 8–9, pp. 907–917. doi 10.1007/s00024-010-0087-z

    Article  Google Scholar 

  • Narteau, C., Byrdina, S., Shebalin, P., and Schorlemmer, D, Common dependence on stress for the two fundamental laws of statistical seismology, Nature, 2009, vol. 462, no. 2, pp. 642–645.

    Article  Google Scholar 

  • Ogata, Y, Seismicity analysis through point-process modeling: a review, Pure Appl. Geophys., 1999, vol. 155, nos. 2–4, pp. 471–507.

    Article  Google Scholar 

  • Prozorov, A.G., Dynamic algorithm for aftershock elimination from the global earthquake catalog, in Matematicheskie metody v seismologii i geodinamike. Vychislitel’naya seismologiya, vyp. 19 (Mathematical Methods in Seismology and Geodynamics. Computational Seismology, vol. 19), Moscow: Nauka, 1986, pp.58–62.

    Google Scholar 

  • Reasenberg, P., Second-order moment of Central California seismicity, 1969–1982, J. Geophys. Res., 1985, vol. 90, no. B7, pp. 5479–5495.

    Article  Google Scholar 

  • Schorlemmer, D., Gerstenberger, M., Wiemer, S., Jackson, D.D., and Rhoades, D.A, Earthquake likelihood model testing, Seismol. Res. Lett., 2007, vol. 78, pp. 17–29.

    Article  Google Scholar 

  • Shebalin, P., Zaliapin, I., and Keilis-Borok, V, Premonitory raise of the earthquakes’ correlation range: Lesser Antilles, Phys. Earth Planet. Int., 2000, vol. 122, pp. 241–249.

    Article  Google Scholar 

  • Shebalin, P., Narteau, C., Holschneider, M., and Schorlemmer, D., Short-term earthquake forecasting using early aftershock statistics, Bull. Seimol. Soc. Am., 2011, vol. 101, no. 4, pp. 297–312.

    Article  Google Scholar 

  • Shebalin, P., Narteau, C., Holschneider, M., and Zechar, J, Combining earthquake forecast models using differential probability gains, Earth, Planets Space, 2014, vol. 66, no. 37, pp. 1–14.

    Google Scholar 

  • Smirnov, V.B, Prognostic anomalies of seismic regime. I. Technique for preparation of original data, Geofiz. Issled., 2009, vol. 10, no. 2, pp. 7–22.

    Google Scholar 

  • Smirnov, V.B., Ponomarev, A.V., Bernard, P., and Patonin, A.V, Regularities in transient modes in the seismic process according to the laboratory and natural modeling, Izv., Phys. Solid Earth, 2010, vol. 46, no. 2, pp. 104–135.

    Article  Google Scholar 

  • Soloviev, A.A., Gorshkov, A.I., Novikova, O.V., Gvishiani, A.D., and Dobrovolsky, M.N, Recognition of earthquake-prone areas: methodology and analysis of the results, Izv., Phys. Solid Earth, 2014, vol. 50, no. 2, pp. 151–168.

    Article  Google Scholar 

  • Stiphout, T., Zhuang, J., and Marsan, D., Seismicity declustering. Community Online Resource for Statistical Seismicity Analysis. 2012. http://www.corssa.org. doi 10.5078/corssa-52382934

  • Tsuboi, C, Earthquake energy, earthquake volume, aftershock area, and strength of the Earth’s crust, J. Phys. Earth, 1956, vol. 4, pp. 63–66.

    Google Scholar 

  • Utsu, T.A, Statistical study on the occurrence of aftershocks, Geophys. Mag., 1961, vol. 30, pp. 521–605.

    Google Scholar 

  • Vorobieva, I.A, Prediction of a subsequent strong earthquake, Phys. Earth Planet. Inter., 1999, vol. 111, pp. 197–206.

    Article  Google Scholar 

  • Vorobieva, I., Narteau, C., Shebalin, P., Beauducel, F., Nercessian, A., Clouard, V., and Bouin, M.P, Multiscale mapping of completeness magnitude of earthquake catalogs, Bull. Seismol. Soc. Am., 2013, vol. 103, no. 4, pp. 2188–2202.

    Article  Google Scholar 

  • Wells, D.L. and Coppersmith, K.J, New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement, Bull. Seismol. Soc. Am., 1994, vol. 84, no. 4, pp. 974–1002.

    Google Scholar 

  • Wiemer, S. and Wyss, M, Minimum magnitude of completeness in earthquake catalogs: examples from Alaska, the western United States, and Japan, Bull. Seismol. Soc. Am., 2000, vol. 90, no. 4, pp. 859–869.

    Article  Google Scholar 

  • Zechar, J.D. and Jordan, T.H, Testing alarm-based earthquake predictions, Geophys. J. Int., 2008, vol. 172, pp. 715–724.

    Article  Google Scholar 

  • Zechar, J.D., Gerstenberger, M.C., and Rhoades, D.A, Likelihood-based tests for evaluating space-rate-magnitude earthquake forecasts, Bull. Seismol. Soc. Am., 2010, vol. 100, no. 3, pp. 1184–1195. doi 10.1785/0120090192

    Article  Google Scholar 

  • Zhuang, J., Ogata, Y., and Vere-Jones, D, Analyzing earthquake clustering features by using stochastic reconstruction, J. Geophys. Res., 2004, vol. 109, no. B5, pp. 1–17. doi 10.1029/2003JB002879

    Article  Google Scholar 

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Correspondence to S. V. Baranov.

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Original Russian Text © S.V. Baranov, P.N. Shebalin, 2017, published in Fizika Zemli, 2017, No. 3, pp. 43–61.

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Baranov, S.V., Shebalin, P.N. Forecasting aftershock activity: 2. Estimating the area prone to strong aftershocks. Izv., Phys. Solid Earth 53, 366–384 (2017). https://doi.org/10.1134/S1069351317020021

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