Research is aimed at developing methodological principles for preliminary detection of the seismic signal arrival registered by a three-component seismic station (TCSS), taking into account polarization properties of background and signal components. Methods. Seismic signals were recorded using the GURALP CMG seismic observation network of the Main Special Control Center (MSCC) of the State Space Agency (SSA) of Ukraine. Result. The main difference between a signal component of a three-component seismic record and a background is polarization properties. Considering these characteristics makes it possible to detect seismic signals and determine their components. Traditional methods for analyzing polarization in a three-component seismic record often involve significant computational effort and are typically employed for processing and analyzing seismic data in real time. In this study, we propose a new approach that evaluates the linearity of the implemented methods and determines the angles of seismic wave arrivals. This is particularly crucial for monitoring potential emergency sources, such as hazardous objects and seismically active areas. Our method can also be applied in real-time scenarios. Scientific novelty. Considering the properties of polarization, as opposed to relying solely on amplitude detection criteria, enables the detection of signals with a lower signal-to-noise ratio. This increases the sensitivity of the Transient Coherent Seismic Source (TCSS) to magnitudes. By utilizing polarization analysis in seismic signal detection, we not only enhance detection capabilities but also gain additional information about the parameters of seismic signal components, such as their azimuth and angle of arrival at the surface. This information can be instrumental in identifying the seismic signal components and determining the location of the seismic event source in relation to the observation point (OP). Significance of research. This approach makes it possible to increase the magnitude sensitivity of OP and the observation system as a whole. The relative simplicity of implementation makes it possible to apply it in real time. Determining angular characteristics of seismic wave arrival allows applying the proposed approach in a continuous monitoring loop for potential emergency sources.
- Alkaz, V. G., Onofraš, N. I., & Perel'berg, A. I. (1977). Polarization analysis of seismic oscillations. Shtiints publishing house. (in Russian).
- Bataille, K., & Chiu, J. M. (1991). Polarization analysis of high-frequency, three-component seismic data. Bulletin of the Seismological Society of America, 81(2), 622-642.
- Gordienko, V. O., Gordienko, Yu. O., & Kyrylyuk, V. A. (2010). Detection of seismic signals and determination of angular characteristics of their sources based on the results of polarization filtering. Bulletin of ZhDTU. Series "Technical Sciences", (1 (52)), 67-71. (in Ukrainian).
- Gordienko, Yu. O., Solonets, O. I., Koshel, A. V., & Rudenko, D. V. (2017). Analysis of methods for detecting seismic signals based on the results of observations by a three-component seismic station. Collection of scientific papers of the Kharkiv Air Force University, (2), 107-110. (in Ukrainian).
- Gordienko, Yu. O. (2011). Polarization filtering of measurement data of a three-component seismic station. Bulletin of ZhDTU. Series "Technical Sciences", (3 (58)), 123-127. (in Ukrainian). https://doi.org/10.26642/tn-2011-3(58)-123-127
- Kosulina N. G., Lyashenko G. A., Zotova, O. S., Polyanova, N. V. (2020). Least squares method. Teaching and methodological manual. Kharkiv. KhNTUSG. 25 p. (in Ukrainian).
- Li, J., He, M., Cui, G., Wang, X., Wang, W., & Wang, J. (2020). A novel method of seismic signal detection using waveform features. Applied Sciences, 10(8), 2919. https://doi.org/10.3390/app10082919
- Liashchyk, O. I., & Karyagin, Y. V. (2018). Peculiarities of seismicity in the region of the Argentine Islands archipelago, caused by iceberg formation processes. Ukrainian Antarctic Journal, (1 (17)), 32-39. (in Ukrainian). https://doi.org/10.33275/1727-7485.1(17).2018.29
- Mashkov O. A., Kirilyuk V.A. (2002a). Scientific problems of creating an automated seismic data processing system (algorithmic aspects. Special technique and weaponry. No. 1,2, 35-41. (in Russian).
- Mashkov O. A., Kyrylyuk V. A. (2002b). Methodology for detecting seismic signals. Proceedings of the Academy of Defense of Ukraine. No. 35, 122–131. (in Ukrainian).
- Pichugin, M. F., Mashkov, O. A., Sashchuk, I. M., & Kyrylyuk, V. A. (2006). Processing of geophysical signals in modern automated complexes: a textbook. (in Ukrainian).
- Rivero-Moreno, C., & Escalante-Ramirez, B. (1996, June). Seismic signal detection with time-frequency models. In Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96) (pp. 345-348). IEEE. https://doi.org/10.1109/TFSA.1996.547484
- Trnkoczy, A. (2009). Understanding and parameter setting of STA/LTA trigger algorithm. In New manual of seismological observatory practice (NMSOP) (pp. 1-20). Deutsches GeoForschungsZentrum GFZ. https://doi.org/10/2312/CFZ.NMSOP_R1_IS_8.1
- Vakaliuk, T. A., Pilkevych, I., Hordiienko, Y., & Loboda, V. (2023, May). Application of Polarization-Time Model Seismic Signal for Remote Monitoring of Potential Sources Emergencies by Three-Component Seismic Station. In CMIS (pp. 52-64). https://ceur-ws.org/Vol-3392/paper5.pdf
- Vakaliuk, T. A., Pilkevych, I. A., Hordiienko, Y. O., Loboda, V. V., & Saliy, A. O. (2023). Detection of a seismic signal by a three-component seismic station and determination of the seismic event center. Radio Electronics, Computer Science, Control, (4), 175-175. https://doi.org/10.15588/1607-3274-2023-4-16
- Vashchenko, V. M., Tolchonov, I. V., Gordienko, Yu. O., & Solonets, O. I. (2012). Statement of the problem of detecting emergency hazard factors by seismic means. Information Processing Systems, (2), 280-284. (in Ukrainian).
- Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., & Trujillo, J. (1998). A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88(1), 95-106. https://doi.org/10.1785/BSSA0880010095
- Zhao, Y., Niu, F., Zhang, Z., Li, X., Chen, J., & Yang, J. (2021). Signal detection and enhancement for seismic crosscorrelation using the wavelet-domain Kalman filter. Surveys in Geophysics, 42, 43-67.). https://doi.org/10.1007/s10712-020-09620-6