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Problem of Recognition of Strong-Earthquake-Prone Areas: a State-of-the-Art Review

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Abstract—The paper addresses almost half a century’s development of pattern recognition algorithms’ application for solving the problem of determination of strong earthquake-prone areas. This approach was named Earthquake-Prone Areas (EPA). The pattern recognition algorithms applied for this purpose, the studied regions, and the methods for assessing the reliability of the obtained results including the theory of dynamic and limit recognition problems are considered. A recently developed alternative method for solving the problem by identifying the clusters of earthquake epicenters is also presented. This method is based on the approaches of the Discrete Mathematical Analysis (DMA) and implemented in the form of the algorithmic system named Formalized Clustering and Zoning (FCAZ). The comparison of the results obtained by the EPA approach and FCAZ system shows their good consistency which provides an additional argument in favor of their reliability. The possibilities for further development and joint application of the EPA approach and FCAZ system and creating, on this basis, an integrated method for systems analysis with the inclusion of artificial intelligence are outlined. In case of success, this method is expected to be used in seismic hazard assessment and planning of earthquake-resistant construction.

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Funding

The work was carried in partial fulfillment of the research tasks specified by the state contracts of the Geophysical Center RAS and the Institute of Earthquake Prediction Theory and Mathematical Geophysics RAS and approved by the Ministry of Science and Education of the Russian Federation.

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Authors and Affiliations

  1. Geophysical Center, Russian Academy of Sciences, 119296, Moscow, Russia

    A. D. Gvishiani & B. A. Dzeboev

  2. Institute of Earthquake Prediction Theory and Mathematical Geophysics, Russian Academy of Sciences, 117997, Moscow, Russia

    A. A. Soloviev

  3. Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, 123242, Moscow, Russia

    A. D. Gvishiani

  4. Geophysical Institute, Vladikavkaz Scientific Center, Russian Academy of Sciences, 362002, Vladikavkaz, Russia

    B. A. Dzeboev

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  1. A. D. Gvishiani
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  2. A. A. Soloviev
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Gvishiani, A.D., Soloviev, A.A. & Dzeboev, B.A. Problem of Recognition of Strong-Earthquake-Prone Areas: a State-of-the-Art Review. Izv., Phys. Solid Earth 56, 1–23 (2020). https://doi.org/10.1134/S1069351320010048

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