ON THE MONITORING OF SEISMIC ACTIVITY USING THE ALGORITHMS OF DISCRETE MATHEMATICAL ANALYSIS
Abstract and keywords
Abstract (English):
The paper presents a new mathematical approach, entitled Seismic Activity monitoRing by Discrete mathematical analysis (SARD) and aimed at seismic level assessment. It is based on application of well-proven algorithms of Discrete Mathematical Analysis (DMA) for the study of earthquake catalogs. The possibility of applying the proposed method for the territory of California, Kamchatka Peninsula and the Caucasus is shown. Evaluation of efficiency of the developed method is carried out using an error diagram.

Keywords:
Seismic activity monitoring, discrete mathematical analysis (DMA), fuzzy measure, error diagram, Seismic Activity monitoRing by Discrete mathematical analysis (SARD), earthquake catalog
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References

1. Abubakirov, I. R., Gusev, A. A. , Guseva, E. M. , Pavlov, V. M. , Skorkina, A. A. Mass determination of moment magnitudes $M_w$ and establishing the relationship between $M_w$ and $M_L$ for moderate and small Kamchatka earthquakes, // Izvestiya, Physics of the Solid Earth, 2018. - v. 54 - no. 1 - p. 33.; DOI: https://doi.org/10.1134/S1069351318010019; EDN: https://elibrary.ru/UYJNWL

2. Agayan, S. M., Soloviev, A. A. Allocation of dense areas in metric spaces basing on crystallization, // System Research and Information Technologies, 2004. - no. 2 - p. 7.; EDN: https://elibrary.ru/RZYRGP

3. Agayan, S. M., Bogoutdinov, S. R., Dobrovolsky, M. N. Discrete perfect sets and their application in cluster analysis, // Cybernetics and Systems Analysis, 2014. - v. 50 - no. 2 - p. 176.; DOI: https://doi.org/10.1007/s10559-014-9605-9; EDN: https://elibrary.ru/UGIWML

4. Bormann, P. (ed.) New Manual of Seismological Observatory Practice 2 (NMSOP-2) - Potsdam: Deutsches GeoForschungsZentrum., 2012.

5. Dzeboev, B. A. A New Approach to Monitoring Seismic Activity: California Case Study, // Doklady Earth Sciences, 2017. - v. 473, Part 1 - p. 338.; DOI: https://doi.org/10.1134/S1028334X17030126; EDN: https://elibrary.ru/LUJMGH

6. Fedotov, S. A., Solomatin, A. V. The long-term earthquake forecast for the Kuril-Kamchatka island arc for the September 2013 to August 2018 period; the seismicity of the arc during preceding deep-focus earthquakes in the sea of Okhotsk (in 2008, 2012, and 2013 at $M = 7.7$, 7.7, and 8.3), // Journal of Volcanology and Seismology, 2015. - v. 9 - no. 2 - p. 65.; DOI: https://doi.org/10.1134/S0742046315020025; EDN: https://elibrary.ru/SFGCHF

7. GS RAN, (publ.) Earthquakes in the Northern Eurasia in 1992-2008 - Obninsk: GS RAN, Moscow, OIFZ RAN, 1997 - Obninsk, 1997-2013., 2013.

8. Gvishiani, A., Dubois, J. Artificial intelligence and dynamic systems for geophysical applications - Paris: Springer-Verlag., 2002. - 350 pp.; DOI: https://doi.org/10.1007/978-3-662-04933-4; EDN: https://elibrary.ru/RTDWVH

9. Gvishiani, A. D., Dzeboev, B. A. Assessment of seismic hazard in choosing of a radioactive waste disposal location, // Mining Journal, 2015. - no. 10 - p. 39.

10. Gvishiani, A., et al. Identification of a geological region for earthquakes using syntactic pattern recognition of seismograms, // Natural Hazards, 1994. - v. 10 - p. 139.; DOI: https://doi.org/10.1007/BF00643448; EDN: https://elibrary.ru/XOLHJV

11. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R., Ledenev, A. V., Zlotniki, Z., Bonnin, Z. Mathematical Methods of Geoinformatics. II. Fuzzy-Logic Algorithms in the Problems of Abnormality Separation in Time Series, // Cybernetics and Systems Analysis, 2003. - v. 39 - no. 4 - p. 555.; DOI: https://doi.org/10.1023/B:CASA.0000003505.56410.4f; EDN: https://elibrary.ru/LHZMEP

12. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R., Tikhotsky, S., Hinderer, J., Bonnin, J., Diament, M. Algorithm FLARS and recognition of time series anomalies, // System Research [ampersand] Information Technologies, 2004. - no. 3 - p. 7.; EDN: https://elibrary.ru/RZYRKL

13. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R. Discrete mathematical analysis and monitoring of volcanoes, // Inzh. Ekol., 2008a. - no. 5 - p. 26.; EDN: https://elibrary.ru/RZYRLP

14. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R. Fuzzy recognition of anomalies in time series, // Doklady Earth Sciences, 2008b. - v. 421 - no. 1 - p. 838.; DOI: https://doi.org/10.1134/S1028334X08050292; EDN: https://elibrary.ru/LKXKYV

15. Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R., Zlotnicki, J., Bonnin, J. Mathematical methods of geoinformatics. III. Fuzzy comparisons and recognition of anomalies in time series, // Cybernetics and Systems Analysis, 2008c. - v. 44 - no. 3 - p. 309.; DOI: https://doi.org/10.1007/s10559-008-9009-9; EDN: https://elibrary.ru/LLCORT

16. Gvishiani, A. D., Belov, S. V. , Agayan, S. M. , Rodkin, M. V., Morozov, V. N., Tatarinov, V. N., Bogoutdinov, Sh. R. Geoinformation technologies: artificial intelligence methods in the assessment of tectonic stability of Nizhnekanskii Massif, // Inzh. Ekol., 2008d. - no. 2 - p. 3.; EDN: https://elibrary.ru/RZYRUB

17. Gvishiani, A. D., Dobrovolsky, M. N., Agayan, S. M., Dzeboev, B. A. Fuzzy-based clustering of epicenters and strong earthquake-prone areas, // Environmental Engineering and Management Journal, 2013a. - v. 12 - no. 1 - p. 1.; EDN: https://elibrary.ru/QBBLTV

18. Gvishiani, A., Dzeboev, B., Agayan, S. A new approach to recognition of the earthquake-prone areas in the Caucasus, // Izvestiya, Physics of the Solid Earth, 2013b. - v. 49 - no. 6 - p. 747.; DOI: https://doi.org/10.1134/s1069351313060049; EDN: https://elibrary.ru/RWLSEH

19. Gvishiani, A. D., Agayan, S. M., Dobrovolsky, M. N., Dzeboev, B. A. Objective classification of the epicenters and recognition of the earthquake-prone areas in California, // Geoinformatika, 2013c. - no. 2 - p. 44.; EDN: https://elibrary.ru/QCOVVP

20. Gvishiani, A. D., Dzeboev, B. A., Agayan, S. M. FCAZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian mountain belts, // Izvestiya. Physics of the Solid Earth, 2016. - v. 52 - no. 4 - p. 461.; DOI: https://doi.org/10.1134/S1069351316040017; EDN: https://elibrary.ru/WVENKT

21. Gvishiani, A. D., Agayan, S. M. , Dzeboev, B. A. , Belov, I. O. Recognition of Strong Earthquake-Prone Areas with a Single Learning Class, // Doklady Earth Sciences, 2017a. - v. 474, Part 1 - p. 546.; DOI: https://doi.org/10.1134/S1028334X17050038; EDN: https://elibrary.ru/XNIKPW

22. Gvishiani, A. D., Dzeboev, B. A. , Belov, I. O. , Sergeyeva, N. A., Vavilin, E. V. Successive Recognition of Significant and Strong Earthquake-Prone Areas: The Baikal-Transbaikal Region, // Doklady Earth Sciences, 2017b. - v. 477, Part 2 - p. 1488.; DOI: https://doi.org/10.1134/S1028334X1712025X; EDN: https://elibrary.ru/YOIWJN

23. Gvishiani, A. D., Dzeboev, B. A., Sergeyeva, N. A., Rybkina, A. I. Formalized Clustering and the Significant Earthquake-Prone Areas in the Crimean Peninsula and Northwest Caucasus, // Izvestiya. Physics of the Solid Earth, 2017c. - v. 53 - no. 3 - p. 353.; DOI: https://doi.org/10.1134/S106935131703003X; EDN: https://elibrary.ru/XNHFQA

24. Keilis-Borok, V., Ismail-Zadeh, A., Kossobokov, V., Shebalin, P. Non-linear dynamics of the lithosphere and intermediate-term earthquake prediction, // Tectonophysics, 2001. - v. 338 - no. 3-4 - p. 247.; DOI: https://doi.org/10.1016/S0040-1951(01)00080-4; EDN: https://elibrary.ru/LGWPSR

25. Kossobokov, V., Shebalin, P. Earthquake Prediction // Nonlinear Dynamics of the Lithosphere and Earthquake Prediction, Keilis-Borok V. I. and Soloviev A. A. (eds.), Springer Series in Synergetics - Berlin, Heidelberg: Springer., 2003. - p. 141.

26. Laverov, N. P., Malovichko, A. A., Starovoit, O. E. Russian Network of Seismological Observations: Status and Prospects of Development // Materials of International Conference ``Seismicity of Northern Eurasia'', July 28-31, 2008 - Obninsk: GS RAS., 2008. - p. 5.

27. Levina, V. I., Lander, A. V. , Mityushkina, S. V. , Chebrova, A. Yu. The seismicity of the Kamchatka region: 1962-2011, // Journal of Volcanology and Seismology, 2013. - v. 7 - no. 1 - p. 37.; DOI: https://doi.org/10.1134/S0742046313010053; EDN: https://elibrary.ru/RFDFQD

28. Mikhailov, V. O., Galdeano, A. , Diament, M. , Gvishiani, A. D., Agayan, S. M., Bogoutdinov, Sh. R., Graeva, E. M., Sailhac, P. Application of artificial intelligence for Euler solutions clustering, // Geophysics, 2003. - v. 68 - no. 1 - p. 168.; DOI: https://doi.org/10.1190/1.1543204; EDN: https://elibrary.ru/ERLVKP

29. Molchan, G. Structure of optimal strategies in earthquake prediction, // Tectonophysics, 1991. - v. 193 - no. 4 - p. 267.; DOI: https://doi.org/10.1016/0040-1951(91)90336-Q; EDN: https://elibrary.ru/XOSJYF

30. Nauka, (publ.) Earthquakes in USSR in 1962-1991 - Moscow: Nauka., 1997.

31. Novikova, O. V., Rotvain, I. M. Advance earthquake prediction by algorithm CN, // Doklady Akademii Nauk, 1996. - v. 348 - no. 4 - p. 548.

32. Rautian, T. G., et al. Origins and methodology of the Russian energy K-class system and its relationship to magnitude scales, // Seismological Research Letters, 2007. - v. 78 - p. 579.

33. Riznichenko, Yu. V. Seismic activity and energy of maximal earthquakes // Problems on Geophysics of Central Asia and Kazakhstan - Moscow: Nauka., 1967. - p. 36.

34. Shebalin, P. N. A Methodology for Prediction of Large Earthquakes with Waiting Times Less than One Year, // Vychislitel'naya Seismologiya, 2006. - no. 37 - p. 7.

35. Shebalin, P., et al. Short-term earthquake forecasting using early aftershock statistics, // Bulletin of the Seismological Society of America, 2011. - v. 101 - no. 1 - p. 297.; DOI: https://doi.org/10.1785/0120100119; EDN: https://elibrary.ru/OIBWUJ

36. Shebalin, P., Narteau, C., Holschneider, M., Zechar, J. Combining earthquake forecast models using differential probability gains, // Earth, Planets and Space, 2014. - v. 66 - no. 37 - p. 1.

37. Soloviev, An., et al. Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory, // Earth Planets Space, 2012a. - v. 64 - no. 9 - p. 743.; DOI: https://doi.org/10.5047/eps.2012.03.004; EDN: https://elibrary.ru/RGCFEZ

38. Soloviev, An. A., Agayan, S. M., Gvishiani, A. D., Bogoutdinov, Sh. R., Chulliat, A. Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms, // Izvestiya, Physics of the Solid Earth, 2012b. - v. 48 - no. 5 - p. 395.; DOI: https://doi.org/10.1134/S106935131204009X; EDN: https://elibrary.ru/PDPDWV

39. Soloviev, A., et al. Mathematical Tools for Geomagnetic Data Monitoring and the INTERMAGNET Russian Segment, // Data Science Journal, 2013. - v. 12 - p. WDS114.

40. Soloviev, A. A., Gvishiani, A. D., Gorshkov, A. I., Dobrovolsky, M. N., Novikova, O. V. Recognition of earthquake-prone areas: Methodology and analysis of the results, // Izvestiya, Physics of the Solid Earth, 2014. - v. 50 - no. 2 - p. 151.; DOI: https://doi.org/10.1134/S1069351314020116; EDN: https://elibrary.ru/SKPUND

41. Soloviev, A. A., Zharkikh, J. I., Krasnoperov, R. I., Nikolov, B. P., Agayan, S. M. GIS-oriented solutions for advanced clustering analysis of geoscience data using ArcGIS platform, // Russian Journal of Earth Sciences, 2016. - v. 16 - p. 151.; DOI: https://doi.org/10.2205/2016ES000587; EDN: https://elibrary.ru/XESXYH

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