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GIS-Oriented Database on Seismic Hazard Assessment for Caucasian and Crimean Regions

  • METHODS AND MEANS OF SATELLITE DATA PROCESSING AND INTERPRETATION
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Abstract

Zones of higher seismic hazard occupy about 20% of Russia’s territory, and 5% are characterized by extremely high hazard. These latter are, in particular, regions of Caucasus and Crimea with an aggregate population of about 15 M people. In order to assess seismic hazard and to minimize the consequences of possible earthquakes in these regions, a special-purpose database has been created for these regions; this database and a multifunctional user interface for its operation are currently being developed. For the first time, one software environment has integrated the most complete results on recognizing zones of higher seismicity by independent methods and the initial data on which the recognition was based. Thus, the system allows integrated multi-criteria seismic hazard assessment in a given region. The use of a modern geographic informational system (GIS) has made the preparation, organization, and analysis of these data considerably easier. The GIS makes it possible on the basis of a comprehensive approach to seismic hazard assessment to group and visualize the respective data in an interactive map. The analytical and interactive query tools integrated in the GIS allow a user to assess the degree of risk in regions under consideration based on different criteria and methods. The seismic hazard assessment database and its user interface were achieved using ESRI ArcGIS software, which completely satisfies the scaling requirement in terms of both functionality and data volume.

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REFERENCES

  1. Agayan, S.M., Bogoutdinov, Sh.R., Soloviev, A., and Sidorov, R., The study of time series using the DMA methods and geophysical applications, Data Sci. J., 2016, vol. 15, no. 16, pp. 1–21. doi 10.5334/dsj-2016-016

    Article  Google Scholar 

  2. Alekseevskaya, M.A., Gabrielov, A.M., Gvishiani, A.D., Gel’fand, I.M., and Rantsman, E.Ya., Morphostructural zoning of mountainous regions according to formalized features, in Raspoznavanie i spektral’nyi analiz v seismologii (Recognition and Spectral Analysis in Seismology), Keilis-Borok, V.I., Ed., Moscow: Nauka, 1977, pp. 33–49.

    Google Scholar 

  3. Alekseevskaya, M., Gabrielov, A., Gel’fand, I., Gvishiani, A., and Rantsman, E., Formal morphostructural zoning of mountain territories, J. Geophys., 1977, vol. 43, pp. 227–233.

    Google Scholar 

  4. ArcGIS World Imagery. http://goto.arcgisonline.com/ maps/World_Imagery. Accessed February 12, 2018.

  5. Beriozko, A.E., Soloviev, A.A., Gvishiani, A.D., Zhalkovskii, E.A., Krasnoperov, R.I., Smagin, S.A., and Bolotskii, E.S., Intellectual geographical information system “Earth Science Data for the Territory of Russia”, Inzh. Ekol., 2008, no. 5, pp. 32–40.

  6. Beriozko, A., Lebedev, A., Soloviev, A., Krasnoperov, R., and Rybkina, A., Geoinformation system with algorithmic shell as a new tool for earth sciences, Russ. J. Earth. Sci., 2011, vol. 12, ES1001. doi 10.2205/2011ES000501

    Article  Google Scholar 

  7. Bondur, V.G., Modern approaches to processing large hyperspectral and multispectral aerospace data flows, Izv., Atmos. Ocean. Phys., 2014, vol. 50, no. 9, pp. 840–852. doi 10.7868/S0205961414010035

    Article  Google Scholar 

  8. Bondur, V.G. and Zverev, A.T., A method of earthquake forecast based on the lineament analysis of satellite images, Dokl. Earth Sci., 2005, vol. 402, no. 4, pp. 561–567.

    Google Scholar 

  9. Bondur, V.G. and Zverev, A.T., Mechanisms of lineament formation recorded on space images from monitoring of earthquake-endangered areas, Issled. Zemli Kosmosa, 2007, no. 1, pp. 47–56.

  10. Bondur, V.G., Zverev, A.T., and Gaponova, E.V., Lineament analysis of space imagery of seismic areas of Russia, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2012, vol. 9, no. 4, pp. 213–222.

    Google Scholar 

  11. ESRI Shapefile Technical Description, Redlands, Calif.: ESRI, 1998.

  12. Förste, C., Bruinsma, S.L., Shako, R., et al., A new release of EIGEN-6: The latest combined global gravity field model including LAGEOS, GRACE and GOCE data from the collaboration of GFZ, Potsdam and GRGS Toulouse, Geophys. Res. Abstr., 2012, vol. 14, EGU2012-2821.

    Google Scholar 

  13. Fu, P., Getting to Know Web GIS, Redlands, California, USA: ESRI Press, 2016.

    Google Scholar 

  14. Gel’fand, I.M., Guberman, Sh.A., Izvekova, M.L., Keilis-Borok, V.I., and Rantsman, E.Ya., On criteria of high seismicity, Dokl. Akad. Nauk SSSR, 1972, vol. 202, no. 6, pp. 1317–1320.

    Google Scholar 

  15. Gorshkov, A.I., Raspoznavanie mest sil’nykh zemletryasenii v Al’piisko–Gimalaiskom poyase (Recognition of Places of Strong Earthquakes in the Alpine–Himalayan Belt), Moscow: KRASAND, 2010.

  16. Gorshkov, A.I., Soloviev, A.A., and Zharkikh, Yu.I., A morphostructural zoning of the Mountainous Crimea and the possible locations of future earthquakes, J. Volcanol. Seismol., 2017, vol. 11, no. 6, pp. 407–413. doi 10.7868/S0203030617060025

    Article  Google Scholar 

  17. Gvishiani, A.D. and Soloviev, A.A., Association of the epicenters of strong earthquakes with the intersection of morphostructural lines in South America, in Metody i algoritmy interpretatsii seismologicheskikh dannykh (Methods and Algorithms for Seismological Data Interpretation), Keilis-Borok, V.I. and Levshin, A.L., Eds., Moscow: Nauka, 1981, pp. 46–50.

  18. Gvishiani, A.D., Agayan, S.M., Bogoutdinov, Sh.R., Ledenev, A.V., Zlotniki, Z., and Bonnin, Z., Mathematical methods of geoinformatics. II. Fuzzy-logic algorithms in the problems of abnormality separation in time series, Cybern. Syst. Anal., 2003, vol. 39, no. 4, pp. 555–563.

    Article  Google Scholar 

  19. Gvishiani, A.D., Agayan, S.M., Bogoutdinov, Sh.R., Tikhotsky, S.A., Hinderer, J., Bonnin, J., and Diament, M., Algorithm FLARS and recognition of time series anomalies, Syst. Res. Inf. Technol., 2004, no. 3, pp. 7–16.

  20. Gvishiani, A.D., Agayan, S.M., Bogoutdinov, Sh.R., and Soloviev, A.A., Discrete mathematical analysis and geological–geophysical applications, Vestn. Kamchatskoi Reg. Assots. Uchebno-Nauchnyi Tsentr, Nauki Zemle, 2010, vol. 16, no. 2, pp. 109–125.

    Google Scholar 

  21. Gvishiani, A.D., Dzeboev, B.A., and Agayan, S.M., A new approach to recognition of the strong earthquake-prone areas in the Caucasus, Izv., Phys. Solid Earth, 2013a, vol. 49, no. 6, pp. 747–766. doi 10.7868/ S0002333713060045

    Article  Google Scholar 

  22. Gvishiani, A., Dobrovolsky, M., Agayan, S., and Dzeboev, B., Fuzzy-based clustering of epicenters and strong earthquake-prone areas, Environ. Eng. Manage. J., 2013b, vol. 12, no. 1, pp. 1–10.

    Article  Google Scholar 

  23. Gvishiani, A.D., Dzeboev, B.A., and Agayan, S.M., FCaZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian mountain belts, Izv., Phys. Solid Earth, 2016, vol. 52, no. 4, pp. 461–491.

    Article  Google Scholar 

  24. Gvishiani, A.D., Dzeboev, B.A., Agayan, S.M., and Belov, I.O., Recognition of strong earthquake-prone areas with a single learning class, Dokl. Earth Sci., 2017a, vol. 474, no. 1, pp. 546–551. doi 10.7868/ S0869565217130175

    Article  Google Scholar 

  25. Gvishiani, A.D., Dzeboev, B.A., Sergeeva, N.A., and Rybkina, A.I., Formalized clustering and significant earthquake-prone areas in the Crimean Peninsula and Northwest Caucasus, Izv., Phys. Solid Earth, 2017b, vol. 53, no. 3, pp. 353–365. doi 10.7868/S0002333717030036

    Article  Google Scholar 

  26. Ismail-Zadeh, A., Le Mouël, J.-L., Soloviev, A., Tapponnier, P., and Vorobieva, I., Numerical modeling of crustal block-and-fault dynamics, earthquakes and slip rates in the Tibet–Himalayan region, Earth Planet. Sci. Lett., 2007, vol. 258, nos. 3–4, pp. 465–485. doi 10.1016/j.epsl.2007.04.006

    Article  Google Scholar 

  27. Kosobokov, V.G., Prognoz zemletryasenii i geodinamicheskie protsessy (Earthquake Prediction and Geodynamic Processes), vol. 1: Prognoz zemletryasenii: osnovy, realizatsiya, perspektivy (Earthquake Prediction: Basics, Implementation, and Prospects), Moscow: GEOS, 2005.

  28. Krasnoperov, R.I. and Soloviev, A.A., Analytical geoinformation system for integrated geological and geophysical studies on the territory of Russia, Gorn. Zh., 2015. no. 10, pp. 89–93. doi 10.17580/gzh.2015.10.16

    Article  Google Scholar 

  29. Krasnoperov, R.I., Soloviev, A.A., Nikolov, B.P., Zharkikh, Yu.I., Grudnev, A.A., Interactive web-application for integrated study of spatial data on Earth sciences using the database of the Geophysical Center of the Russian Academy of Sciences, Issled. Geoinf., 2016, vol. 4, no. 1. doi 10.2205/2016BS039

  30. Kuznetsov, N.A. and Gitis, V.G., Network analytical GIS in basic research, Inf. Protsessy, 2004, vol. 4, no. 3, pp. 221–240.

    Google Scholar 

  31. Lesur, V., Hamoudi, M., Choi, Y., Dyment, J., and Thebault, E., Building the second version of the World Digital Magnetic Anomaly Map (WDMAM), Earth Planets Space, 2016, vol. 68, no. 27, pp. 1–13. doi 10.1186/s40623-016-0404-6

    Article  Google Scholar 

  32. Mitchell, A., The ESRI Guide to GIS Analysis, vol. 2, Redlands, Calif.: ESRI, 2005.

    Google Scholar 

  33. Nekrasova, A.K. and Kosobokov, V.G., Unified scaling law for earthquakes in Crimea and Northern Caucasus, Dokl. Earth Sci., 2016, vol. 470, no. 4, pp. 1056–1058.

    Article  Google Scholar 

  34. Nekrasova, A., Kossobokov, V., Peresan, A., Aoudia, A., and Panza, G.F., A multiscale application of the unified scaling law for earthquakes in the central Mediterranean area and Alpine region, Pure Appl. Geophys., 2011, vol. 168, nos. 1–2, pp. 297–327.

    Article  Google Scholar 

  35. Nikolov, B.P., Zharkikh, J.I., Soloviev, A.A., Krasnoperov, R.I., and Agayan, S.M., Integration of data mining methods for earth science data analysis in GIS environment, Russ. J. Earth Sci., 2015, vol. 15, no. ES4004. doi 10.2205/2015ES000559

  36. Parvez, I.A., Nekrasova, A., and Kossobokov, V., Estimation of seismic hazard and risks for the Himalayas and surrounding regions based on unified scaling law for earthquakes, Nat. Hazards, 2014, vol. 71, no. 1, pp. 549–562.

    Article  Google Scholar 

  37. Rantsman, E.Ya., Mesta zemletryasenii i morfostruktura gornykh stran (Earthquake Places and Morphostructure of Mountainous Countries), Moscow: Nauka, 1979.

  38. Shako, R., Förste, C., Abrikosov, O., Bruinsma, S., Marty, J.-C., Lemoine, J., Flechtner, F., Neumayer, H., and Dahle, C., EIGEN-6C: A high-resolution global gravity combination model including GOCE data, in Observation of the System Earth from Space: CHAMP, GRACE, GOCE and Future Missions, Flechtner, F., Sneeuw, N., and Schuh, W.-D., Eds., Berlin: Springer, 2013, pp. 155–161. doi 10.1007/978-3-642-32135-1_20

    Google Scholar 

  39. Soloviev, A.A., Simulation of the dynamics of block-and-fault systems and seismicity, Tr. Inst. Mat. Mekh. Ural. Otd. Ross. Akad. Nauk, 2011, vol. 17, no. 2, pp. 174–190.

    Google Scholar 

  40. Soloviev, A.A. and Gorshkov, A.I., Modeling the dynamics of the block structure and seismicity of the Caucasus, Izv., Phys. Solid Earth, 2017, vol. 53, no. 3, pp. 321–331.

    Article  Google Scholar 

  41. Soloviev, A.A., Novikova, O.V., Gorshkov, A.I., and Piotrovskaya, E.P., Recognition of potential sources of strong earthquakes in the Caucasus region using GIS technologies, Dokl. Earth Sci., 2013, vol. 450, no. 2, pp. 658–660. doi 10.7868/S0869565213170222

    Article  Google Scholar 

  42. Soloviev, A.A., Gvishiani, A.D., Gorshkov, A.I., Dobrovolsky, M.N., and Novikova, O.V., Recognition of earthquake-prone areas: Methodology and analysis of the results, Izv., Phys. Solid Earth, 2014, vol. 50, no. 2, pp. 151–168. doi 10.7868/S0002333714020112

    Article  Google Scholar 

  43. Soloviev, A.A., Zharkikh, J.I., Krasnoperov, R.I., Nikolov, B.P., and Agayan, S.M., GIS-oriented solutions for advanced clustering analysis of geoscience data using ArcGIS platform, Russ. J. Earth. Sci., 2016, vol. 16, ES6004. doi 10.2205/2016ES000587

    Article  Google Scholar 

  44. Soloviev, A.A., Krasnoperov, R.I., Nikolov, B.P., Zharkikh, Yu.I., and Agayan, S.M., Web-oriented software system for analysis of spatial geophysical data using geoinformatics methods, Issled. Zemli Kosmosa, 2018, no. 2, pp. 65–76.

  45. Soloviev, Al.A., Gorshkov, A.I., and Soloviev, An.A., Application of the data on the lithospheric magnetic anomalies in the problem of recognizing the earthquake prone areas, Izv., Phys. Solid Earth, 2016, vol. 52, no. 6, pp. 803–809.

    Article  Google Scholar 

  46. Ulomov, V.I., Update of normative seismic zoning in the framework of the integrated information system for the seismic safety of Russia, Seism. Instrum., 2013, vol. 49, no. 2, pp. 87–114.

    Article  Google Scholar 

  47. Ulomov, V.I. and Bogdanov, M.I., A new set of maps of general seismic zoning for the territory of Russian Federation (GSZ-2012), Inzh. Izyskaniya, 2013, no. 8, pp. 8–17.

  48. Ulomov, V.I., and the Working Group of the GSHAP Reg. 7, Seismic hazard of northern Eurasia, Ann. Geop-hys., 1999, vol. 42, pp. 1023–1038.

    Google Scholar 

  49. World Imagery Map Contributors. http://esriurl.com/ WorldImageryContributors. Accessed February 12, 2018.

  50. Zandbergen, P.A., Python Scripting for ArcGIS, Redlands, Calif.: ESRI, 2014.

    Google Scholar 

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ACKNOWLEDGMENTS

Facilities of the Center of Collective Use “Analytical Center of Geomagnetic Data” based at GC RAS were used in the research. The work was carried out in the framework of a State Contract from the Ministry of Science and Higher Education of the Russian Federation.

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Authors and Affiliations

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

    An. A. Soloviev, Al. A. Soloviev, A. D. Gvishiani, B. P. Nikolov & Yu. I. Nikolova

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

    An. A. Soloviev & A. D. Gvishiani

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

    Al. A. Soloviev

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  1. An. A. Soloviev
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  2. Al. A. Soloviev
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  3. A. D. Gvishiani
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  4. B. P. Nikolov
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Correspondence to Yu. I. Nikolova.

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Translated by N. Astafiev

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Soloviev, A.A., Soloviev, A.A., Gvishiani, A.D. et al. GIS-Oriented Database on Seismic Hazard Assessment for Caucasian and Crimean Regions. Izv. Atmos. Ocean. Phys. 54, 1363–1373 (2018). https://doi.org/10.1134/S0001433818090505

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