The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
Source abstract
Abstract
This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The objective of this study is to evaluate the effectiveness of the Shannon spectral entropy method in detecting and assessing short-term seismic events through a seismogram analysis. This method has demonstrated sensitivity to variations in the spectral characteristics of the registered signals. A threshold value for the increase in spectral entropy information has been pinpointed for reliable P-wave detection. The results could be applied in real-time automated seismic monitoring systems. In addition to the conventional spectral analysis techniques, the proposed methodology may serve as the input to the neural network models used in seismological applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Seismology and Earthquake Studies · Earthquake Detection and Analysis · Seismic Waves and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71/23 Al-Farabi Ave., Almaty 050040, Kazakhstan
Data Science & Astrophysics Research Center, Almaty 050040, Kazakhstan
Funding
Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan
References (27)
Show source-provided references
1. Japan’s Earthquake Early Warning System on 11 March 2011: Performance, Shortcomings, and Changes10.1193/1.4000127 ↗
2. Wu, Y.M., and Mittal, H. (2021). A Review on the Development of Earthquake Warning System Using Low-Cost Sensors in Taiwan. Sensors, 21.10.3390/s21227649 ↗
3. Efficient GPU-accelerated seismic analysis strategy and scenario simulation for large-scale nuclear structure cluster-soil interaction over ten million DOFs10.1016/j.compgeo.2024.106583 ↗
4. Crustal Flow-Induced Earthquake Revealed by Full-Waveform Tomography and Implications for Prehistoric Civilization Destruction10.1029/2024JB029745 ↗
5. Earthquake early warning: Recent advances and perspectives10.1016/j.earscirev.2020.103184 ↗
6. Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning10.1029/2023JB026575 ↗
7. Choi, S., Lee, B., Kim, J., and Jung, H. (2024). Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images. Electronics, 13.10.3390/electronics13010229 ↗
8. Peng, P., He, Z., Wang, L., and Jiang, Y. (2020). Microseismic records classification using capsule network with limited training samples in underground mining. Sci. Rep., 10.10.1038/s41598-020-70916-z ↗
9. Li, J., Hao, M., and Cui, Z. (2024). A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods. Appl. Sci., 14.10.3390/app14051997 ↗
10. Ponce-Flores, M., Frausto-Solís, J., Santamaría-Bonfil, G., Pérez-Ortega, J., and González-Barbosa, J.J. (2020). Time Series Complexities and Their Relationship to Forecasting Performance. Entropy, 22.10.3390/e22010089 ↗
11. Improving onset picking in ultrasonic testing by using a spectral entropy criterion10.1121/10.0024337 ↗
12. Silva, S.D., Carvalho, P., Da Costa, C., Medeiros, J., and Corso, G. (2020, January 8–11). Automatic Microseismic Event Detection in Noisy Environments from Unfiltered Seismic Signals Using Instantaneous Spectral Shannon Entropy. Proceedings of the EAGE 2020 Annual Conference & Exhibition Online, Online.10.3997/2214-4609.202011156 ↗
13. Microseismic event detection in noisy environments with instantaneous spectral Shannon entropy10.1103/PhysRevE.106.014133 ↗
14. Fluctuations of the entropy change under time reversal: Further investigations on identifying the occurrence time of an impending major earthquake10.1209/0295-5075/130/29001 ↗
15. Zhexebay, D., Skabylov, A., Ibraimov, M., Khokhlov, S., Agishev, A., Kudaibergenova, G., Orazakova, A., and Agishev, A. (2025). Deep Learning for Early Earthquake Detection: Application of Convolutional Neural Networks for P-Wave Detection. Appl. Sci., 15.10.3390/app15073864 ↗
16. (2025, June 24). LTD Seismological Experience and Methodology Expedition of the Committee of Science of the Ministry of Education and Science of the Republic of Kazakhstan. Seismic Network of the Seismological Experience and Methodology Expedition CS MES RK, Available online: https://www.fdsn.org/networks/detail/QZ/.
17. Central Asian Institute for Applied Geosciences (2025, June 24). Central Asian Seismic Network of CAIAG, Available online: https://www.fdsn.org/networks/detail/KC/.
18. Kyrgyz Institute of Seismology, IVTAN/KIS, and University of California, San Diego (2025, June 24). Kyrgyz Seismic Telemetry Network, Available online: https://www.fdsn.org/networks/detail/KN/.
19. Kyrgyz Institute of Seismology, KIS (2025, June 24). Kyrgyz Digital Network, Available online: https://www.fdsn.org/networks/detail/KR/.
20. Spectral entropy: A complementary index for rolling element bearing performance degradation assessment10.1243/09544062JMES1224 ↗
21. A Review of Gear Fault Diagnosis Using Various Condition Indicators10.1016/j.proeng.2016.05.131 ↗
22. Weaver, K., Waheed, K., and Salem, F. (2003, January 20–24). An entropy based robust speech boundary detection algorithm for realistic noisy environments. Proceedings of the International Joint Conference on Neural Networks, Portland, OR, USA.
23. Su, Y., Zeng, P., Yao, W., Gao, J., Zhou, C., Duan, J., Zhang, Y., Han, B., Zhu, W., and Chen, T. (2025). Investigating the dynamic characteristics of crack formation of Jiangxi lateritic soil subjected to drying-wetting cycles. J. Saf. Sustain., in press.10.1016/j.jsasus.2025.06.005 ↗
24. Local and regional P-wave spectral attenuation model for Iran10.1093/gji/ggaa450 ↗
25. Self-organized criticality and earthquake predictability: A long-standing question in the light of natural time analysis10.1209/0295-5075/132/29001 ↗
26. Time-frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia10.1111/j.0001-5172.2004.00323.x ↗
27. Informational and entropic criteria of self-similarity of fractals and chaotic signals10.26577/ijmph.2018.v9i1.251 ↗