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Classification of Tectonic and Nontectonic Earthquakes by an Integrated Learning Algorithm

  • Tao Ren
  • Pengyu Wang
  • Mengnan Lin
  • Xiaoyu Liu
  • Hongfeng ChenEmail author
  • Jie Liu
Article
  • 21 Downloads

Abstract

A machine learning model for accurate and quick classification of tectonic and nontectonic earthquakes is proposed. Firstly, an improved method is proposed for detection of first arrivals. An iterative approach is applied for the multiwindow algorithm to decrease its computational cost, then a new method for detection of first arrivals is proposed by combining the improved multiwindow algorithm with a recursive least-squares filter and the Akaike information criterion. Secondly, it is shown that the integrated learning algorithm is a suitable method for classification, then four suitable features are manually selected to train it. Thirdly, the three parameter values of the integrated learning model are determined to improve its accuracy. Finally, based on seismic data from the China Earthquake Networks Center, simulations are conducted to test the validity of the proposed method for detection of first arrivals, then the classification accuracy is tested. The results show an accuracy of the model of 88.88%, indicating effective classification performance.

Keywords

Integrated learning algorithm detection of first arrivals tectonic earthquake nontectonic earthquake seismic attribute 

Notes

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (61473073, 61104074), Fundamental Research Funds for the Central Universities (N161702001, N171706003, 182608003, 181706001), and Program for Liaoning Excellent Talents in University (LJQ2014028).

References

  1. Baziw, E., & Weir-Jones, I. (2002). Application of Kalman filtering techniques for microseismic event detection. In The Mechanism of Induced Seismicity  (pp. 449–471). Basel: Birkhäuser.Google Scholar
  2. Capilla, C. (2006). Application of the Haar wavelet transform to detect microseismic signal arrivals. Journal of applied geophysics, 59(1), 36–46.CrossRefGoogle Scholar
  3. Chen, Z., & Stewart, R. (2005). Multi-window algorithm for detecting seismic first arrivals. In Abstracts, CSEG National Convention (pp. 355–358).Google Scholar
  4. Coppens, F. (1985). First arrival picking on common-offset trace collections for automatic estimation of static corrections. Geophysical Prospecting, 33, 1212–1231.CrossRefGoogle Scholar
  5. Daizhi, L., Renming, W., Xihai, L., & Zhigang, L. (2005). On set point identification of single-channel seismic signal based on wavelet packet and the AR model. Chinese Journal of Geophysics, 48(5), 1098–1102.Google Scholar
  6. Fabio, B., Mike, D. D., & Ron, D. M. (1996). A fractal-based algorithm for detecting first arrivals on seismic traces. Geophysics, 61(4), 1095–1102.CrossRefGoogle Scholar
  7. Flora, G., Antonietta, M. E., & Patrizia, R. (2017). Fast discrimination of local earthquakes using a neural approach. Seismological Research Letters, 88(4), 1089–1096.CrossRefGoogle Scholar
  8. Gelchinsky, B., & Shtivelman, V. (1983). Thinking automatic picking of first arrivals and parameterization of travel time curves. Geophysical Prospecting, 31, 915–928.CrossRefGoogle Scholar
  9. Liu, H., & Zhang, J. (2014). STA/LTA algorithm analysis and improvement of Microseismic signal automatic detection. Progress in Geophysics, 29(4), 1708–1714.Google Scholar
  10. Hanming, H., Yinju, B., Shijun, L., Zhengfeng, J., & Rui, L. (2010). A wavelet feature research on seismic waveforms of earthquakes and explosions. Acta Seismologica Sinica, 32(3), 270–276.Google Scholar
  11. Huijian, L., Runqiu, W., Siyuan, C., et al. (2016). Weak signal detection using multiscale morphology in microseismic monitoring. Journal of Applied Geophysics, 133, 39–49.CrossRefGoogle Scholar
  12. Ji, W., Jiuhui, C., Qiyuan, L., Shuncheng, L., & Biao, G. (2006). Automatic onset phase picking for portable seismic array observation. Acta Seismologica Sinica, 28(1), 42–51.Google Scholar
  13. Jingsong, L., Yun, W., & Zhenxing, Y. (2013). On micro-seismic first arrival identification: a case study. Chinese Journal of Geophysics, 56(5), 1660–1666.Google Scholar
  14. Joseph, B. M., & Douglas, R. S. (1999). First-break timing: arrival onset times by direct correlation. Geophysics, 64(5), 1492–1501.CrossRefGoogle Scholar
  15. Küperkoch, L., Meier, T., Lee, J., et al. (2010). Automated determination of P-phase arrival times at regional and local distances using higher order statistics. Geophysical Journal International, 181(2), 1159–1170.Google Scholar
  16. MAEDA, N. (1985). A method for reading and checking phase time in auto-processing system of seismic wave data. Zisin (Journal of the Seismological Society of Japan. 2nd ser.), 38(3), 365–379.CrossRefGoogle Scholar
  17. McCormack, M. D., Zaucha, D. E., & Dushek, D. W. (1993). First-break refraction event picking and seismic data trace editing using neural networks. Geophysics, 58(1), 67–78.CrossRefGoogle Scholar
  18. Mingxia, B., Hanming, H., Yinju, B., Rui, L., Yinyan, C., & Zhao, J. (2011). A study on seismic signal HHT features extraction and SVM recognition of earthquake and explosion. Progress in Geophysics, 26(4), 1157–1164.Google Scholar
  19. Perol, T., Gharbi, M., & Denolle, M. (2018). Convolutional neural network for earthquake detection and location. Science Advances, 4(2), e1700578.CrossRefGoogle Scholar
  20. Rongsheng, Z., Zhifeng, D., Qingju, W., & Jianping, W. (2000). Seismological evidences for the multiple incomplete crustal subductions in Himalaya and southern Tibet. Chinese Journal of Geophysics, 43(6), 780–797.Google Scholar
  21. St-Onge A. (2010). Akaike information criterion applied to detecting first arrival times on microseismic data. SEG Technical Program Expanded Abstracts, 1658–1662.Google Scholar
  22. Taylor, S. R. (2011). Statistical discriminants from two-dimensional grids of regional P/S spectral ratios. Bulletin of the Seismological Society of America, 101(4), 1584–1589.CrossRefGoogle Scholar
  23. Tingting, W., & Yinju, B. (2011). Criterion selection of earthquake and explosion recognition. Seismological and Geomagnetic Observation and Research, 32(6), 62–67.Google Scholar
  24. Wong, J., Han, L., Bancroft, J., & Stewart, R. (2009). Automatic time-picking of first arrivals on noisy microseismic data. CSEG. 0 0.2 0.4 0.6 0.8, 1(1.2), 1–4.Google Scholar
  25. Xibing, L., Xueyi, S., Morales-Esteban, A., & Zewei, W. (2017). Identifying P-phase arrival of weak events: The Akaike information criterion picking application based on the empirical mode decomposition. Computers & Geosciences, 100, 57–66.CrossRefGoogle Scholar
  26. Xibing, L., Xueyi, S., Zewei, W., & Longjun, D. (2016). Identifying P-phase arrivals with noise: An improved kurtosis method based on DWT and STA/LTA. Journal of Applied Geophysics, 133, 50–61.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tao Ren
    • 1
  • Pengyu Wang
    • 1
  • Mengnan Lin
    • 1
  • Xiaoyu Liu
    • 2
  • Hongfeng Chen
    • 2
    Email author
  • Jie Liu
    • 2
  1. 1.Software College of Northeastern UniversityShenyangChina
  2. 2.China Earthquake Networks CenterBeijingChina

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