An Investigation on Signal Comparison by Measuring of Numerical Strings Similarity

  • Alexander SmaglichenkoEmail author
  • Tatyana A. Smaglichenko
  • Arkady Genkin
  • Boris Melnikov
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)


The counter algorithm has been presented to detect pairs of similar numerical strings in order to distinguish between a subset of identical signals and other signals. The pair of similar signals is determined using the matrix of the algorithm. Two elements of the matrix estimate the similarity degree in contrast to the ordinary applied a single value of correlation coefficient. The matching of signal images with the matrix elements has been made on an example of impulse signals. Using this data type we compare the outcomes of two methods: a counter based technique and the correlation method. The difference between the method proposed and the correlation method is discussed.


Time series Similar numerical strings Signal processing 



We thank anonymous reviewers for constructive critics that helped to improve the initial version of the paper.

The work was carried out within the framework of the state projects No. 0139-2019-0009, No. 10.331-17, No. 5.6370.2017/BCh.


  1. 1.
    Caffagni, E., Eaton, D.W., Jones, J.P., van der Baan, M.: Detection and analysis of microseismic events using a matched filtering algorithm (MFA). Geophys. J. Int. 206(1), 644–658 (2016)Google Scholar
  2. 2.
    Shtun, S.Y., Golenkin, M.Y., Shtun, A.S., Shabalinskaya, D.D., Cheprasov, A.V., Kuzakov, V.R., Brichikova, M.P., Zolotoi, N.V.: New approach to offshore field development in russia: ultra deep LWD measurements for accurate 3D reservoir model update. Soc. Pet. Eng. (2017).
  3. 3.
    Keranen, K.M., Weingarten, M.: Induced seismicity. Annu. Rev. Earth Planet. Sci. 46, 149–174 (2018). Scholar
  4. 4.
    Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S., Gimbert, F., Massey, C.: Environmental seismology: what can we learn on earth surface processes with ambient noise? J. Appl. Geophys. 116, 62–74 (2015). Scholar
  5. 5.
    Dietze, M.: The R package “eseis” - a software toolbox for environmental seismology. Surf. Dynam. 6, 669–686 (2018). Scholar
  6. 6.
    Eisner, L., Hulsey, B.J., Duncan, P., Jurick, D., Werner, H., Keller, W.: Comparison of surface and borehole locations of induced seismicity. Geophy. Prospect. 58, 809–820 (2010). Scholar
  7. 7.
    Kapetanidis, V., Papadimitriou, P.: Estimation of arrival-times in intense seismic sequences using a Master-Events methodology based on waveform similarity. Geophys. J. Int. 187, 889–917 (2011). Scholar
  8. 8.
    Cieplicki, R., Eisner, L., Mueller, M.: Microseismic event detection: comparing P-wave migration with P- and S-wave crosscorrelation. In: SEG Denver 2014 Annual Meeting, pp. 2168–2172 (2014).
  9. 9.
    Akram, J., Eaton, D.W.: A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 81(2), 71–91 (2016). Scholar
  10. 10.
    Anikiev, D., Valenta, J., Stanek, F., Eisner, L.: Joint location and source mechanism inversion of microseismic events: benchmarking on seismicity induced by hydraulic fracturing. Geophys. J. Int. 198, 249–258 (2014). Scholar
  11. 11.
    Stanek, F., Anikiev, D., Valenta, J., Eisner, L.: Semblance for microseismic event detection. Geophys. J. Int. 201, 1362–1369 (2015). Scholar
  12. 12.
    Hasselman, K.: Statistical analysis of generation of microseisms. Reverend Geophys. 1(2), 177–210 (1963)CrossRefGoogle Scholar
  13. 13.
    Oliver, J.: Worldwide, storm of microseism from the period of about 27 seconds. Bull. Seism. Soc. 52, 307–517 (1963)Google Scholar
  14. 14.
    Barstow, N., et al.: Particle motion and pressure relationship of the ocean bottom at 3900 m depth: 0.003 to 5 Hz. Geophys. Res. Lett. 16, 1185–1188 (1989)CrossRefGoogle Scholar
  15. 15.
    Shapiro, N.M., et al.: High-resolution surface-wave tomography from ambient seismic noise. Science 307(5715), 1615–1618 (2005)CrossRefGoogle Scholar
  16. 16.
    Sch\(\ddot{o}\)pa, A., Chao, W.A., Lipovsky, B., Hovius, N., White, R.S., Green, R.G., Turowski, J.M.: Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath. Earth Surf. Dynam. 6, 467–485 (2018).
  17. 17.
    Gimbert, F., Tsai, V.C., Lamb, M.P.: A physical model for seismic noise generation by turbulent flow in rivers. J. Geophys. Res. 119, 2209–2238 (2014). Scholar
  18. 18.
    Sens-Schoenfelder, C., Larose, E.: Temporal changes in the lunar soil from correlation of diffuse vibrations. Phys. Rev. E. 78, 045601 (2008). Scholar
  19. 19.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis. Forecasting and Control. Holden-Day, San Francisco (1970)zbMATHGoogle Scholar
  20. 20.
    Kashyap, R.L., Rao, A.R.: Dynamic Stochastic Models from Empirical Data. Academic Press. N.Y., San Francisco (1976)Google Scholar
  21. 21.
    Krautkramer, I., Krautkramer, H.: Werkstoffprufunq mit ultraschall. Sprinqer Verlaq, New York (1975)CrossRefGoogle Scholar
  22. 22.
    Smaglichenko, A.V., Sayankina, M.K., Smaglichenko, T.A., Volodin, I.A.: Physical experiments and stochastic modeling to clarify the system containing the seismic source and the ground. In: ISCS 2014 International Symposium of Complex Systems, vol. 14, pp. 125–135 (2015)Google Scholar
  23. 23.
    Smaglichenko, A.V., Smaglichenko, T.A., Sayankina, M.K.: An approach to developing greedy algorithms of picking undistorted data in the tasks of seismic exploration. Int. Sci. J. Appl. Discret. Math. Heuristic Algorithms 1(1), 42–51 (2015). Samara University PressGoogle Scholar
  24. 24.
    Bendat, J.S., Piersol, A.G.: Engineering applications of correlation and spectral analysis. Wiley-Interscience, New York (1980)zbMATHGoogle Scholar
  25. 25.
    Matlab copyright. Version (R2015b) (2015)Google Scholar
  26. 26.
    Smaglichenko, A.V., Bjarnason, I.Th.: Consecutive Analysis based on the branch and bound method applied to picking P- and S- wave arrival times. Abstract in Materials of “The Science Day of the School of Engineering and Natural Science of the University of Iceland” (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alexander Smaglichenko
    • 1
    • 4
    Email author
  • Tatyana A. Smaglichenko
    • 2
  • Arkady Genkin
    • 1
    • 5
  • Boris Melnikov
    • 3
  1. 1.V.A. Trapeznikov Institute of Control SciencesRussian Academy of SciencesMoscowRussia
  2. 2.Research Oil and Gas InstituteRussian Academy of SciencesMoscowRussia
  3. 3.Russian State Social UniversityMoscowRussia
  4. 4.Institute of Seismology and GeodynamicsV.I. Vernadsky Crimean Federal UniversitySimferopolRussia
  5. 5.National University of Science and Technology MISISMoscowRussia

Personalised recommendations