DOI QR코드

DOI QR Code

Akaike Information Criterion (AIC)를 이용한 경산 지진관측소 P파와 S파 도착시간 자동추정

Onset Time Estimation of P- and S-waves at Gyeongsan Seismic Station Using Akaike Information Criterion (AIC)

  • 권조아 (부산대학교 지질환경과학과) ;
  • 강수영 (부산대학교 지질재해산업자원연구소) ;
  • 김광희 (부산대학교 지질환경과학과)
  • Kwon, Joa (Department of Geological Sciences, Pusan National University) ;
  • Kang, Su Young (Institute of Geological Hazard and Industrial Resources, Pusan National University) ;
  • Kim, Kwang-Hee (Department of Geological Sciences, Pusan National University)
  • 투고 : 2018.10.10
  • 심사 : 2018.12.19
  • 발행 : 2018.12.31

초록

P파와 S파의 도착시간 정보는 지진 발생위치 결정, 1차원 및 3차원 지하구조 등 지진학 연구 수행에 중요한 정보이다. 최근 지진관측소의 수가 비약적으로 증가함에 따라 관측망을 운영하면서 수동으로 지진파의 도착시간을 측정하는 것은 상당한 시간이 소요되는 일이 되었다. 본 연구에서는 진원요소에 대한 사전정보(지진 발생위치와 시간)를 확보할 수 있는 경우 Akaike Information Criterion (AIC)을 적용하여 추가의 관측소에서 국지지진의 P파와 S파의 도착시간을 자동측정하였다. 해당 방법을 경산(DAG2) 지진관측소에 기록된 자료에 적용한 후 수동 측정한 값과 자동 측정한 값을 비교한 결과 P파의 경우 95.1%, S파의 경우 93.7%가 0.1초 이하의 차이를 보이면서 결정되는 것을 확인하였다. 자동측정결과의 높은 정확성은 향후 고밀도 지진관측망 운영에 성공적으로 적용될 수 있음을 시사한다.

The onset times of P- and S-waves are important information to have reliable earthquake locations, 1D or 3D subsurface velocity structures, and other related studies in seismology. As the number of seismic stations increases significantly in recent years, it becomes a formidable task for network operators to pick phase arrivals manually. This study used a simple method to estimate additional P- and S-wave arrival times for local earthquakes when a priori information (event location and time) is available using the Akaike Information Criterion (AIC). We applied the AIC program to the earthquake data recorded at the seismic station located in Gyeongsan (DAG2). The comparisons of automatically estimated phase arrival times with manually picked onset times showed that 95.1% and 93.7% of P-wave and S-wave arrival time estimations, respectively, are less than 0.1 second difference. The higher percentage of agreement presented the method which can be successfully applied to large data sets recorded by high-density seismic arrays.

키워드

참고문헌

  1. Akaike, H., 1973, Information theory and an extension of the maximum likelihood principle, in: Petrov, B.B., Csaki, F. (eds.), 2nd International Symposium on Information theory, Budapest, pp. 267-281.
  2. Allen, R., 1982. Automatic Phase Pickers: Their Present Use and Future Prospects. Bulletin of the Seismological Society of America, 72, S225-S242.
  3. Baer, M., Kradolfer, U., 1987, An automatic phase picker for local and teleseismic events. Bulletin of the Seismological Society of America, 77, 1437-1445.
  4. Han, M., Kim, K.-H., Son, M., Kang, S.Y., Park, J.-H., 2016, Location of Recent Micro-earthquakes in the Gyeongju Area. Geophysics and Geophysical Exploration, 19, 97-104. (in Korean) https://doi.org/10.7582/GGE.2016.19.2.097
  5. Kim, K.-H., Kang, T.-S., Rhie, J., Kim, Y., Park, Y., Kang, S.Y., Han, M., Kim, J., Park, J., Kim, M., Kong, C., Heo, D., Lee, H., Park, E., Park, H., Lee, S.-J., Cho, S., Woo, J.-U., Kim, J., 2016, The 12 September 2016 Gyeongju earthquakes: 2. Temporary seismic network for monitoring aftershocks. Geoscience Journal, 20, 753-757. https://doi.org/10.1007/s12303-016-0034-9
  6. Kim, K.-H., Kim, J., Han, M., Kang, S.Y., Son, M., Kang, T.-S., Rhie, J., Kim, Y., Park, Y., Kim, H.-J., You, Q., Hao, T., 2018, Deep fault plane revealed by highprecision locations of early aftershocks following the 12 September 2016 ML 5.8 Gyeongju, Korea, earthquake. Bulletin of the Seismological Society of America, 108, 517-523. https://doi.org/10.1785/0120170104
  7. Kim, W., 1999, P-wave velocity structure of upper crust in the vicinity of the Yangsan Fault region. Geoscience Journal, 3, 17-22. https://doi.org/10.1007/BF02910230
  8. Li, Z., Peng, Z., 2016, An Automatic Phase Picker for Local Earthquakes with Predetermined Locations: Combining a Signal-to-Noise Ratio Detector with 1D velocity Model Inversion. Seismological Research Letters, 87, 1397-1405. https://doi.org/10.1785/0220160027
  9. Maeda, N., 1985, A method for reading and checking phase times in auto-processing system of seismic data. Zisin, 38, 365-379. https://doi.org/10.4294/zisin1948.38.3_365
  10. Ryoo, Y.-G., 2006, A Study for the Efficient Management Scheme of Seismic Network in Korea and the Realtime Analysis of Earthquake Data, Department of Earth Systems and Environmental Sciences, Chonnam National University, Gwangju, Korea. (in Korean)
  11. Sleeman, R., van Eck, T., 1999, Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Physics of the Earth and Planetary Interiors, 113, 265-275. https://doi.org/10.1016/S0031-9201(99)00007-2
  12. Takanami, T., Kitagawa, G., 1988, A new efficient procedure for the estimation of onset times of seismic waves. Journal of Physics of the Earth, 36, 267-290. https://doi.org/10.4294/jpe1952.36.267
  13. Vandecar, J.C., Crosson, R.S., 1990, Determination of teleseismic relative phase arrival times using multichannel cross-correlation and least squares. Bulletin of the Seismological Society of America, 80, 150-169.
  14. Withers, M., Aster, R., Christoper, Y., 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, 95-106.
  15. Zhang, H., Thurber, C.H., Rowe, C., 2003, Automatic PWave Arrival Detection and Picking with Multiscale Wavelet Analysis for Single-Component Recordings. Bulletin of the Seismological Society of America, 93, 1904-1912. https://doi.org/10.1785/0120020241