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A Fast Algorithm for Classifying Seismic Events Using Distributed Computations in Apache Spark Framework

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Abstract

The main ideas of the development of the software implementation of an algorithm for the fast automatic classification of seismic signals based on diagnostic patterns are described. The process of adaptation and integration of this implementation into the distributed computations system Apache Spark is described in detail. A software solution for the preliminary processing of the signals and optimization of the mathematical model for parallel computations using broadcast variables is presented. Performance tests for the classification algorithm on a set of day-long signals are carried out. The execution time of the algorithm in the context of massively parallel computations was reduced tenfold compared with the sequential execution.

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Notes

  1. The codes of International Registry of Seismograph Stations (IR) are available at http://www.isc.ac.uk/registries/.

REFERENCES

  1. Scarpetta, S., Giudicepietro, F., Ezin, E.C., Petrosino, S., Del Pezzo, E., Martini, M., and Marinaro, M., Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks, Bull. Seism. Soc. Am., 2005, vol. 95, no. 1, pp. 185–196.

    Article  Google Scholar 

  2. Benbrahim, M., Daoudi, A., Benjelloun, K., and Ibenbrahim, A., Discrimination of seismic signals using artificial neural networks, Proc. World Acad. Sci. Eng. Technol., 2005, vol. 4, pp. 4–7.

    Google Scholar 

  3. Diersena, S., Leeb, E.-J., Spearsc, D., Chenb, P., and Wanga, L., Classification of seismic windows using artificial neural networks, Proc. Comput. Sci., 2011, vol. 4, pp. 1572–1581.

    Article  Google Scholar 

  4. Hamer, R.M. and Cunningham, J.W., Cluster analyzing profile data confounded with interrater differences: A comparison of profile association measures, Appl. Psychol. Meas., 1981, vol. 5, pp. 63–72.

    Article  Google Scholar 

  5. Kedrov, E.O. and Kedrov, O.K., Spectral time method of identification of seismic events at distances of 15°–40°, Izv.,Phys. Solid Earth, 2006, vol. 42, no. 5, pp. 398–415.

    Article  Google Scholar 

  6. Langer, H., Falsaperla, S., Powell, T., and Thompson, G., Automatic classification and a posteriori analysis of seismic event identification at Soufrière Hills volcano, Montserrat, J. Volcanology Geotherm. Res., 2006, vol. 153, no. 1, pp. 1–10.

    Article  Google Scholar 

  7. Lyubushin, A.A., Jr., Kaláb, Z., and Častová, N., Application of wavelet analysis to the automatic classification of three-component seismic records, Izv.,Phys. Solid Earth, 2004, vol. 40, no. 7, pp. 587–593.

    Google Scholar 

  8. Musil, M. and Pleginger, A., Discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and kohonen maps, Bull. Seismol. Soc. Am., 1996, vol. 86, no. 4, pp. 1077–1090.

    Google Scholar 

  9. Ryzhikov, G.A., Biryulina, M.S., and Husebye, E.S., A novel approach to automatic monitoring of regional seismic events, IRIS Newsletter, 1996, vol. XV, no. 1, pp. 12–14.

    Google Scholar 

  10. Shimshoni, Y. and Intrator, N., Classification of seismic signals by integrating ensembles of neural networks, IEEE Trans. Signal Proc., 1998, vol. 46, no. 5, pp. 1194–1201.

    Article  Google Scholar 

  11. Ryan, T.M., Borisov, D., Lefebvre, M., and Tromp, J., SeisFlows – flexible waveform inversion software, Comput. & Geosci., 2018, vol. 115, pp. 88–95.

    Article  Google Scholar 

  12. Lesage, P., Interactive Matlab software for the analysis of seismic volcanic signals. Comput. & Geosci., 2009, vol. 35, no. 10, pp. 2137–2144.

    Article  Google Scholar 

  13. Jiang, W., Yu, H., Li, L., and Huang, L., A robust algorithm for earthquake detector, Proc. of the 15 World Conference on Earthquake Engineering, Lisbon, 2012.

  14. Álvarez, I., García, L., Mota, S., Cortés, G., Benítez, C., and De La Torre A., An automatic p-phase picking algorithm basedon adaptive multiband processing, IEEE Geosci. Remote Sensing Lett., 2013, vol. 10, no 6, pp. 1488–1492.

    Article  Google Scholar 

  15. Madureira, G. and Ruano, A., A neural network seismic detector, IFAC Proc. Vol., 2009, vol. 42, no. 19, pp. 304–309.

  16. Clara, E.Y., Ossian, O’R., Karianne, J.B., and Beroza, G.C., Earthquake detection through computationally efficient similarity search, Sci. Advances, 2015, vol. 1, pp. E1501057 (1–13).

  17. Paul, B. Q., Pierre, G., Yoann, C., and Munkhuu, U., Detection and classification of seismic events with progressive multichannel correlation and hidden Markov models, Comput. & Geosci., 2015, vol. 83, pp. 110–119.

    Article  Google Scholar 

  18. IRIS. Incorporated Research Institutions for Seismology. https://www.iris.edu/hq/. Cited May 4, 2019.

  19. Romero, L.E., Titos, M., Bueno, Á., Álvarez, I., García, L., de la Torre, Á, and Benítez, M.C., APASVO: A free software tool for automatic P-phase picking and event detection in seismic traces, Comput. & Geoscie., 2016, vol. 90, Part A, pp. 213–220.

    Google Scholar 

  20. GeoSeisQC. http://www.geoleader.ru/index.php/ru/produkty-ru/ geoseicqc. Cited May 7, 2019.

  21. ZETLAB Дeтeктop STA/LTA. https://zetlab.com/shop/programmnoe-obespechenie/funktsii-zetlab/analiz-signalov/detektor-sta-lta/. Cited May 7, 2019.

  22. Stratimagic. http://www.pdgm.com/products/stratimagic/. Cited May 7, 2019.

  23. Development and creation of GRID applications for solving applied problems of geophysics, project no. 10-07-00491-a, Russian Foundation for Basic Research. http://www.rfbr.ru/rffi/ru/project_search/o_49145. Cited May 7, 2018.

  24. The use of weakly coupled computer systems for solving inverse problems of geophysics, project no. 11-05-00988-a, Russian Foundation for Basic Research. http://www.rfbr.ru/rffi/ru/project_search/o_43212. Cited May 7, 2018.

  25. Development of a GRID system and computation services for analyzing geodynamic space-time processes given Earth remote sensing data from, project no. 11-07-12045-ofi, Russian Foundation for Basic Research. http://www.rfbr.ru/rffi/ru/project_search/o_46676. Cited May 7, 2018.

  26. Distance computations, SciPy.org. https://docs.scipy.org/doc/scipy/reference/spatial.distance.html. Cited May 11, 2018.

  27. Zamaraev, R.Yu., Popov, S.E., and Logov, A.B., The algorithm for classifying seismic events based on the entropy mapping of signals, Izv.,Phys. Solid Earth, 2016, vol. 52, no. 3, pp. 364–370.

    Article  Google Scholar 

  28. Zamaraev, R.Yu. and Popov, S.E., An algorithm for the automatic detection and classification of industrial blasts based on the entropy mapping of signals, Geofiz. Issled., 2019, vol. 20, no. 1, pp. 38–51.

    Google Scholar 

  29. McKay, D., Information Theory, Inference, and Learning Algorithms, Cambridge: Cambridge Univ. Press, 2003.

    Google Scholar 

  30. Kortström, J., Uski, M., and Tiira, T., Automatic classification of seismic events within a regional seismograph network, Comput. & Geosci., 2016, no. 87, pp. 22–30.

  31. Guojun, Gan, Chaoqun, Ma, and Jianhong, Wu., Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability), Philadelphia: Society for Industrial and Applied Mathematics, 2007.

    Google Scholar 

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Funding

This work was supported by the Russian Foundation for Basic Research, project no. 18-07-00013А.

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Authors and Affiliations

  1. Institute of Computer Technologies, Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia

    S. E. Popov & R. Yu. Zamaraev

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  1. S. E. Popov
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  2. R. Yu. Zamaraev
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Correspondence to S. E. Popov or R. Yu. Zamaraev.

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Translated by A. Klimontovich

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Popov, S.E., Zamaraev, R.Y. A Fast Algorithm for Classifying Seismic Events Using Distributed Computations in Apache Spark Framework. Program Comput Soft 46, 35–48 (2020). https://doi.org/10.1134/S0361768820010053

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