Skip to main content

An Investigation on Signal Comparison by Measuring of Numerical Strings Similarity

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 554))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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. 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). https://doi.org/10.2118/187900-MS

  3. Keranen, K.M., Weingarten, M.: Induced seismicity. Annu. Rev. Earth Planet. Sci. 46, 149–174 (2018). https://doi.org/10.1146/annurev-earth-082517-010054

    Article  Google Scholar 

  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). https://doi.org/10.1016/j.jappgeo.2015.02.001

    Article  Google Scholar 

  5. Dietze, M.: The R package “eseis” - a software toolbox for environmental seismology. Surf. Dynam. 6, 669–686 (2018). https://doi.org/10.5194/esurf-6-669-2018

    Article  Google Scholar 

  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). https://doi.org/10.1111/j.1365-2478.2010.00867.x

    Article  Google Scholar 

  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). https://doi.org/10.1111/j.1365-246X.2011.05178.x

    Article  Google Scholar 

  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). https://doi.org/10.1190/segam2014-1614.1

  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). https://doi.org/10.1190/GEO2014-0500.1

    Article  Google Scholar 

  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). https://doi.org/10.1093/gji/ggu126

    Article  Google Scholar 

  11. Stanek, F., Anikiev, D., Valenta, J., Eisner, L.: Semblance for microseismic event detection. Geophys. J. Int. 201, 1362–1369 (2015). https://doi.org/10.1093/gji/ggv070

    Article  Google Scholar 

  12. Hasselman, K.: Statistical analysis of generation of microseisms. Reverend Geophys. 1(2), 177–210 (1963)

    Article  Google Scholar 

  13. Oliver, J.: Worldwide, storm of microseism from the period of about 27 seconds. Bull. Seism. Soc. 52, 307–517 (1963)

    Google Scholar 

  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)

    Article  Google Scholar 

  15. Shapiro, N.M., et al.: High-resolution surface-wave tomography from ambient seismic noise. Science 307(5715), 1615–1618 (2005)

    Article  Google Scholar 

  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). https://doi.org/10.5194/esurf-6-467-2018

  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). https://doi.org/10.1002/2014JF003201

    Article  Google Scholar 

  18. Sens-Schoenfelder, C., Larose, E.: Temporal changes in the lunar soil from correlation of diffuse vibrations. Phys. Rev. E. 78, 045601 (2008). https://doi.org/10.1103/PhysRevE.78.045601

    Article  Google Scholar 

  19. Box, G.E.P., Jenkins, G.M.: Time Series Analysis. Forecasting and Control. Holden-Day, San Francisco (1970)

    MATH  Google Scholar 

  20. Kashyap, R.L., Rao, A.R.: Dynamic Stochastic Models from Empirical Data. Academic Press. N.Y., San Francisco (1976)

    Google Scholar 

  21. Krautkramer, I., Krautkramer, H.: Werkstoffprufunq mit ultraschall. Sprinqer Verlaq, New York (1975)

    Book  Google Scholar 

  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. 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 Press

    Google Scholar 

  24. Bendat, J.S., Piersol, A.G.: Engineering applications of correlation and spectral analysis. Wiley-Interscience, New York (1980)

    MATH  Google Scholar 

  25. Matlab copyright. Version 8.6.0.267246 (R2015b) (2015)

    Google Scholar 

  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 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Smaglichenko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Smaglichenko, A., Smaglichenko, T.A., Genkin, A., Melnikov, B. (2020). An Investigation on Signal Comparison by Measuring of Numerical Strings Similarity. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14907-9_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14906-2

  • Online ISBN: 978-3-030-14907-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics