Algorithms and Measuring Complex for Classification of Seismic Signal Sources, Determination of Distance and Azimuth to the Point of Excitation of Surface Waves
Keywords:
boundary waves, molecular electronics, wavelet analysis, machine learning, azimuth determination, distance determination, data processing algorithmAbstract
Machine learning and digital signal processing methods are used in various industries, including in the analysis and classification of seismic signals from surface sources. The developed wave type analysis algorithm makes it possible to automatically identify and, accordingly, separate incoming seismic waves based on their characteristics. To distinguish the types of waves, a seismic measuring complex is used that determines the characteristics of the boundary waves of surface sources using special molecular electronic sensors of angular and linear oscillations. The results of the algorithm for processing data obtained by the method of seismic observations using spectral analysis based on the Morlet wavelet are presented. The paper also describes an algorithm for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves, considers the use of statistical characteristics and MFCC (Mel-frequency cepstral coefficients) parameters, as well as their joint application. At the same time, the following were used as statistical characteristics of the signal: variance, kurtosis coefficient, entropy and average value, and gradient boosting was chosen as a machine learning method; a machine learning method based on gradient boosting using statistical and MFCC parameters was used as a method for determining the distance to the signal source. The training was conducted on test data based on the selected special parameters of signals from sources of seismic excitation of surface waves. From a practical point of view, new methods of seismic observations and analysis of boundary waves make it possible to solve the problem of ensuring a dense arrangement of sensors in hard-to-reach places, eliminate the lack of knowledge in algorithms for processing data from seismic sensors of angular movements, classify and systematize sources, improve prediction accuracy, implement algorithms for locating and tracking sources. The aim of the work was to create algorithms for processing seismic data for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves.
References
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2. Kurrle D., Igel H., Ferreira A.M.G., Wassermann J., Schreiber U. Can we estimate local Love wave dispersion properties from collocated amplitude measurements of translations and rotations? Geophysical research letters. 2010. vol. 37.
3. Schmelzbach C., Donner S., Igel H., Sollberger D., Taufiqurrahman T., Bernauer F., Häusler M., Van Renterghem C., Wassermann J., Robertsson J. Advances in 6C seismology: Applications of combined translational and rotational motion measurements in global and exploration seismologyю. Geophysics. 2018. vol. 83. pp. WC53-WC69.
4. Li Z., Van der Baan M. Seismology Elastic passive source localization using rotational motion Geophys. J. Int. 2017. vol. 211. pp. 1206–1222.
5. Van Renterghem C., Schmelzbach C., Sollberger D., Robertsson J.O.A. Spatial wavefield gradient-based seismic wavefield separation Geophys. J. Int. 2018. vol. 212. p. 2017.
6. Sollberger D., Greenhalgh S.A., Schmelzbach C., Van Renterghem C., Robertsson J.O.A. 6-C polarization analysis using point measurements of translational and rotational ground-motion: theory and applications. Geophysical Journal International. 2018. vol. 213.
7. Schmidt, R.O, "Multiple Emitter Location and Signal Parameter Estimation," IEEE Trans. Antennas Propagation, 1986. vol. AP-34. pp. 276-280.
8. Trifunac M.D. Effects of torsional and rocking excitations on the response of structures. In Earthquake Source Asymmetry. Structural Media and Rotation Effects, Ed. Teisseyre R., Takeo M., Majewski E., Berlin: Springer, 2006. pp. 569-582.
9. Jalali R.S., Trifunac M.D. Response spectra for near-source, differential and rotational strong motion. Bulletin of the Seismological Society of America. 2009. vol. 99 (2B), pp. 1404-1415.
10. Zembaty Z. Rotational seismic load definition in Eurocode 8, Part 6 for slender, tower-shaped structures. Bulletin of the Seismological Society of America. 2009. vol. 99 (2B), pp. 1483-1485.
11. Gicev V., Trifunac M.D. Transient and permanent rotations in a shear layer excited by strong earthquake pulses. Bulletin of the Seismological Society of America. 2009. vol. 99 (2B), pp. 1391-1403.
12. Trifunac M.D. 75th anniversary of strong motion observation – a historical review. Soil Dynamics and Earthquake Engineering. 2009. vol. 29 (4), pp. 591-606.
13. Shevchenko D.V., Shevchenko V.P. [The choice and optimization of the structure of the construction of autonomous seismic detection devices of the boundary type]. Materialy VIII vserossijskoj nauchno-tehnicheskoj konferencii «Sovremennye ohrannye tehnologii i sredstva obespechenija kompleksnoj bezopasnosti obektov» [Modern security technologies and means of ensuring complex safety of objects: Materials of Conference], 2010. pp. 128-133. (In Russian).
14. Volskov A.A. [The choice of the sampling frequency of the signal for solving the problem of passive seismic direction finding]. Materialy VIII Vserossijskoj nauchno-tehnicheskoj konferencii «Sovremennye ohrannye tehnologii i sredstva obespechenija kompleksnoj bezopasnosti obektov» [Materials of the Conferenc], 2010. pp. 128-133. (In Russ.).
15. Igel H., Bernauer M., Wassermann J., Schreiber K.U. Seismology, rotational, complexity: Encyclopedia of complexity and systems science: Springer Science and Business Media New York, 2015.
16. Lee W.H.K., Igel H., Trifunac M.D. Recent advances in rotational seismology. Seismological Research Letter. 2009. vol. 80. no. 3. pp. 479–490.
17. Van Driel M., Wassermann J., Nader M.F., Schuberth B.S.A., Igel H. Strain rotation coupling and its implications on the measurement of rotational ground motions. Journal of Seismology. 2012. vol. 16. no. 4. pp. 657–668.
18. Muyzert E., Kashubin A., Kragh E., Edme P. Land seismic data acquisition using rotation sensors. 74th EAGE Conference & Exhibition incorporating SPE EUROPEC, 2012. pp. 1–5.
19. Agafonov V.M., Afanasyev K.A., Yashkin A.V. [Determination of the direction to a moving object using a seismic module containing molecular electronic motion sensors]. Trudy MFTI – Proceedings of MIPT. 2013. vol. 5. no. 2. pp. 142-149. (In Russ.).
20. Li Z., Van der Baan M. Enhanced microseismic event localization by reverse time extrapolation. SEG Technical Program Expanded Abstracts. 2015, pp. 4111–4115.
21. Li Z.H., Van der Baan M. Elastic passive source localization using rotational motion. Geophysical Journal International. 2017. vol. 211(2). pp. 1206–1222.
22. Barak O., Herkenhoff F., Dash R., Jaiswal P., Giles J., de Ridder S., Brune R., Ronen S. Six-component seismic land data acquired with geophones and rotation sensors: Wave-mode selectivity by application of multicomponent polarization filtering. The Leading Edge. 2014. vol. 11. pp. 1224–1232.
23. E dme P., Muyzert E. Rotational data measurement. 75th EAGE Conference and Exhibition incorporating SPE EUROPEC, 2013. pp. 1–4.
24. Edme P., Muyzert E. Efficient land seismic acquisition sampling using rotational data. 76th EAGE Conference and Exhibition. Extended abstract, 2014. pp. 1–4.
25. US9766355B2 [Use of vector rotational measurements and vector pressure gradient measurements to enhance spatial sampling of dual-sensor water bottom seismic data]. 19/09/2017. (In Russ.).
26. US9664806B2 [Method to improve spatial sampling of vertical motion of seismic wavefields on the water bottom by utilizing horizontal rotational motion and vertical motion sensors]. 30/05/2017. (In Russ.).
27. CN110612462A [System and method for formation evaluation from a wellbore]. 24/12/2019. (In Russ.).
28. Egorov E., Agafonov V., Avdyukhina S., Borisov S. Angular Molecular–Electronic Sensor with Negative Magnetohydrodynamic Feedback Sensors. 2018. vol. 18, p. 245.
29. Anikin, E. Egorov E., Agafonov V. Mechanical sensors Dependence of Self-Noise of the Angular Motion Sensor Based on the Technology of Molecular-Electronic Transfer, on the Area of the Electrodes. IEE Sensors Lett., 2018. vol. 2. no. 2. pp. 1–4.
30. Zaitsev D., Shabalina A. The features of low-temperature operation for electrochemical sensors of motion parameters for the economic development of the arctic region of the Russian Federation in the fields of geophysics, seismology and seismic exploration, 21st International Multidisciplinary Scientific GeoConference SGEM, 2021. vol. 21, no. 1.1. pp. 759-768.
31. Zaitsev D., Egorov I., Agafonov V. A Comparative Study of Aqueous and Non-Aqueous Solvents to Be Used in Low-Temperature Serial Molecular–Electronic Sensors. Chemosensors, 2022.
32. Egorov E., Shabalina A., Zaitsev D., Kurkov S., Gueorguiev N. Frequency Response Stabilization and Comparative Studies of MET Hydrophone at Marine Seismic Exploration Systems. Sensors, 2020.
33. Chikishev D.A., Zaitsev D.L., Belotelov K.S., Egorov I.V. The Temperature Dependence of Amplitude- Frequency Response of the MET Sensor of Linear Motion in a Broad Frequency Range, in IEEE Sensors Journal, 2019. vol. 19. no. 21. pp. 9653-9661.
34. Neeshpapa A., Antonov A., Zaitsev D., Egorov E., Agafonov V. Geophysical system of permanent installation for underwater monitoring of seismic events, 20th International Multidisciplinary Scientific GeoConference SGEM, 2020. vol. 20. no. 1.3. pp. 17-24.
35. Li M., Shen H., Guo Yu., Mengxiong X. Locating microseismic events using multiplicative time reversal imaging based on decoupled wavefields in 2D VTI media: Theoretical and synthetic cases studies, Journal of Petroleum Science and Engineering. 2021. vol. 202. p. 108547.
36. Sollberger D., Igel H., Schmelzbach C., Edme P., van Manen D.-J., Bernauer F., Yuan S., Wassermann J., Schreiber U., Robertsson J.O.A. Seismological Processing of Six Degree-of-Freedom Ground-Motion Data. Sensors, 2020. vol. 20. p. 6904. doi: 10.3390/s20236904.
37. Pytel W., Fuławka K., Mertuszka P., Pałac-Walko B. Validation of Rayleigh Wave Theoretical Formulation with Single-Station Rotational Records of Mine Tremors in Lower Silesian Copper Basin. Sensors, 2021. vol. 21. p. 3566. doi: 10.3390/s21103566.
38. Available at: http://r-sensors.ru/ru/products/data_loggers/ndas-8226_rus/ (accessed 06/09/2022).
39. Kulesh M.A., Diallo M.S., Holschneider M. [Wavelet analysis of elliptical, dispersive and dissipative properties of Rayleigh waves]. Akusticheskij zhurnal – Acoustic Journal. 2005. vol. 51. no. 4. pp. 500-510. (In Russ.).
40. Davis S., Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1980. vol. 28. no. 4. pp. 357–366.
41. O'Shaughnessy D. Speech Communication: Human and Machine. Addison-Wesley Pub. Co., 1987. p. 150.
42. Oppengejm A.V., Shafer R.V. [Digital Signal Processing] (Russ. ed.: Shac S.Ja.). M.: Svjaz', 1979. (In Russ.).
43. Zheng F., Zhang G., Song Z. COMPARISON OF DIFFERENT IMPLEMENTATIONS OF MFCC J. Computer Science & Technology, 2001. vol. 16(6). pp. 582-589.
44. Xie T., Zheng X., Zhang Y. Seismic facies analysis based on speech recognition feature parameters. Geophysics. 2017. vol. 82. no. 3. pp. 023–035.
45. Friedman J.H. Greedy Function Approximation: A Gradient Boosting Machine, 1999.
46. Zheng F., Zhang G., Song Z. Comparison of different implementations of MFCC J. Computer Science & Technology. 2001. vol. 16(6). pp. 582-589.
47. Powers D.M.W. Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies. 2011.
Published
2022-11-24
How to Cite
Zaitsev, D., Bryksin, V., Belotelov, K., Kompaniets, Y., & Iakovlev, R. (2022). Algorithms and Measuring Complex for Classification of Seismic Signal Sources, Determination of Distance and Azimuth to the Point of Excitation of Surface Waves. Informatics and Automation, 21(6), 1211-1239. https://doi.org/10.15622/ia.21.6.5
Section
Artificial Intelligence, Knowledge and Data Engineering
Copyright (c) Дмитрий Леонидович Зайцев, Виталий Михайлович Брыксин, Константин Сергеевич Белотелов, Юлия Игоревна Компаниец, Роман Никитич Яковлев
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