An improved STA/LTA algorithm for P wave detection in the 2015 Nepal Earthquake
DOI:
https://doi.org/10.61173/c2xxms51Keywords:
Earthquake Prediction, Seismic Monitoring, STA/LTA, Automatic Picking of P-Wave, Nepal EarthquakeAbstract
Automatic identification of seismic phases is one of the important tasks in earthquake rapid reporting and early warning. The STA/LTA method is currently the most widely used automatic pickup method. However, the accuracy and stability of its pickup results heavily depend on the selection of feature functions, time window lengths, and trigger thresholds, so it is to some extent impossible to achieve automatic pickup of P-wave arrival time. We present an improved algorithm for automatically picking up P-wave arrival times. In this scheme, the weight factor K is introduced to construct a new feature function, and a method for finding the maximum value on the STA/LTA curve corresponding to the P-wave arrival time is proposed. This method was applied to measure the arrival of the 2015 Nepal Earthquake to validate previous evaluations. The results show that our improved method has the advantage of eliminating the time wasted on adjusting the trigger threshold when picking up the P-wave, which is necessary for the traditional STA/LTA method, and overcoming the challenge of inaccurate prediction when weak seismic signals with a low signal-to-noise ratio occur.
References
[1] Bai, Y., Jin, X., Wang, X., Su, T., Kong, J., & Lu, Y. (2019). Compound autoregressive network for prediction of multivariate time series. Complexity, 2019, 1-11.
[2] Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Figure 2 The epicenter of Nepal earthquake Sociological methods & research, 33(2), 261-304. https://www.britannica.com/topic/Nepal-earthquake- 3. Grigoli, F., Cesca, S., Krieger, L., Kriegerowski, M., of-2015#/media/1/2024843/197923 Gammaldi, S., Horalek, J., ... & Dahm, T. (2016). Automated The improved method is utilized to review the process microseismic event location using master-event waveform of the 2015 Nepal Earthquake to test its feasibility. The stacking. Scientific reports, 6(1), 25744. new method analyzes the data and creates Figure 3 below. 4. Hendriyana, A., Bauer, K., Muksin, U., & Weber, M. (2018). The figure shows three subplots where the first plot AIC-based diffraction stacking for local earthquake locations at the Sumatran Fault (Indonesia). Geophysical Journal Dean&Francis International, 213(2), 952-962. term earthquake prediction. Scientific reports, 10(1), 21153.
[5] Hossain, A. S. M. F., Adhikari, T. L., Ansary, M. A., & Bari, Q. 11. Trnkoczy, A. (2009). Understanding and parameter setting H. (2015). Characteristics and consequence of Nepal earthquake of STA/LTA trigger algorithm. In New manual of seismological 2015: a review. Geotechnical Engineering Journal of the SEAGS observatory practice (NMSOP) (pp. 1-20). Deutsches & AGSSFA, 46, 114-20. GeoForschungsZentrum GFZ.
[6] LIU Xiao-ming, ZHAO Jun-jie, WANG Yun-min, PENG 12. Vaezi, Y., & Van der Baan, M. (2015). Comparison of the Ping-an. Automatic Picking of Microseismic Events P-wave STA/LTA and power spectral density methods for microseismic Arrivals Based on Improved Method of STA/LTA[J]. Journal of event detection. Geophysical Supplements to the Monthly Northeastern University:Natural Science, 2017, 38(5): 740-745. Notices of the Royal Astronomical Society, 203(3), 1896-1908.
[7] Perol, T., Gharbi, M., & Denolle, M. (2018). Convolutional 13. VOHRADSKY, J. (2001). Neural network model of gene neural network for earthquake detection and location. Science expression. the FASEB journal, 15(3), 846-854. Advances, 4(2), e1700578. 14. WANG Yi-ying, DING Ren-wei, LI Jian-ping, ZHAO Li-
[8] Saqib, M., Şentürk, E., Sahu, S. A., & Adil, M. A. (2021). hong, ZHAO Shuo, ZHANG Shuo-wei. Automatic pickup of Ionospheric anomalies detection using autoregressive integrated microseismic P-wave arrival based on improved STA/LTA and moving average (ARIMA) model as an earthquake precursor. MLoG operators. Journal of Shandong University of Science Acta Geophysica, 69(4), 1493-1507. and Technology: Natural Science, 2021, 40(6): 1-10.
[9] Stevens, J. L., & Adams, D. A. (2000, September). Improved 15. Witze, A. (2015). Major earthquake hits Nepal. Nature, 10. surface wave detection and measurement using phase-matched 16. Wu, Y. M., & Zhao, L. (2006). Magnitude estimation using filtering and improved regionalized models. In Proceedings of the first three seconds P‐wave amplitude in earthquake early the 22nd Annual DoD/DOE Seismic Research Symposium (Vol. warning. Geophysical research letters, 33(16). 1, pp. 145-154). 17. Yoon, C. E., O’Reilly, O., Bergen, K. J., & Beroza, G. C.
[10] Tozzi, R., Masci, F., & Pezzopane, M. (2020). A stress test to (2015). Earthquake detection through computationally efficient evaluate the usefulness of Akaike information criterion in short- similarity search. Science advances, 1(11), e1501057.
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