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Journal of Seismology

, Volume 22, Issue 4, pp 833–840 | Cite as

First arrival time picking for microseismic data based on DWSW algorithm

  • Yue Li
  • Yue Wang
  • Hongbo Lin
  • Tie Zhong
ORIGINAL ARTICLE
  • 135 Downloads

Abstract

The first arrival time picking is a crucial step in microseismic data processing. When the signal-to-noise ratio (SNR) is low, however, it is difficult to get the first arrival time accurately with traditional methods. In this paper, we propose the double-sliding-window SW (DWSW) method based on the Shapiro-Wilk (SW) test. The DWSW method is used to detect the first arrival time by making full use of the differences between background noise and effective signals in the statistical properties. Specifically speaking, we obtain the moment corresponding to the maximum as the first arrival time of microseismic data when the statistic of our method reaches its maximum. Hence, in our method, there is no need to select the threshold, which makes the algorithm more facile when the SNR of microseismic data is low. To verify the reliability of the proposed method, a series of experiments is performed on both synthetic and field microseismic data. Our method is compared with the traditional short-time and long-time average (STA/LTA) method, the Akaike information criterion, and the kurtosis method. Analysis results indicate that the accuracy rate of the proposed method is superior to that of the other three methods when the SNR is as low as − 10 dB.

Keywords

Double-sliding-window SW (DWSW) algorithm Microseismic First arrival time picking SW test Akaike information criterion (AIC) method 

Notes

Funding information

This research is financially supported by the National Natural Science Foundations of China (under grant 41574096).

References

  1. Allen RV (1978) Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am 68:1521–1532Google Scholar
  2. Akram J, David WE (2016) A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 81:71–91CrossRefGoogle Scholar
  3. Akram J, Eaton DW (2016) A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 81:KS71–KS91CrossRefGoogle Scholar
  4. Baziw E, Jones IW (2002) Application of Kalman filtering techniques for microseismic event detection. Pure Appl Geophys 159:449–471CrossRefGoogle Scholar
  5. Baziw E, Nedilko B, Weir-Jones I (2004) Microseismic event detection Kalman filter: derivation of the noise covariance matrix and automated first break determination for accurate source location estimation. Pure Appl Geophys 162:303–329CrossRefGoogle Scholar
  6. Guner B, Frankford MT (2009) A study of the Shapiro-Wilk test for the detection of pulsed sinusoidal radio frequency interference. IEEE Geosci Remote Sens Lett 47:1745–1751CrossRefGoogle Scholar
  7. Kim N (2010) The limit distribution of a modified Shapiro-Wilk statistic for normality to type censored data. J Korean Stat Soc 40:257–266CrossRefGoogle Scholar
  8. Leonard M, Kennett BLN (1999) Multi-component autoregressive techniques for the analysis of seismograms. Phys Earth Planet Inter 113:247–263CrossRefGoogle Scholar
  9. Li XB, Shang XY, Morales-Esteban A, Wang ZW (2017) Identifying P phase arrival of weak events: the Akaike information criterion picking application based on the empirical mode decomposition. Comput Geosci 100:57–66CrossRefGoogle Scholar
  10. Lin HB, Li Y, Zhang C, Ma HT (2015) Curvelet domain denoising based on kurtosis characteristics. J Geophys Eng 12(3):419–426. June (SCI) WOS:000355309000012IF0.778CrossRefGoogle Scholar
  11. Romeu JL, Ozturk A (1993) A comparative study of goodness-of-fit test for multivariate normality. J Multivar Anal 46:309–334CrossRefGoogle Scholar
  12. Royston JP (1982) An extension of Shapiro and Wilks W test for normality to large samples. J R Stat Soc (Applied. Statistics) 31:115–124Google Scholar
  13. Saragiotis CD (2004) Automatic P phase picking using maximum kurtosis and kappa-statistics criteria. IEEE Geosci Remote Sens Lett 1:147–151CrossRefGoogle Scholar
  14. Saragiotis CD, Hadjileontiadis LJ, Panas SM (2002) PAI-S/k: a robust automatic seismic P phase arrival identification scheme. IEEE Geosci Remote Sens Lett 40:1395–1404CrossRefGoogle Scholar
  15. Shapiro SS, Wilk MB (1965) An analysis of variance test for normality(complete samples). Biometrika 52:591–611CrossRefGoogle Scholar
  16. Warpinski N (2009) Microseismic monitoring: inside and out. J Petroleum Technol 61:80–85CrossRefGoogle Scholar
  17. Xia S, Wang WB, Li SR, Wang ZW (2016) Applicaton of Kalman filter in microseismic data denoising based on identified signal model. In: Proceedings of the 28th Chinse control and decision conference, ISSN: 1948-9439Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Communication EngineeringJilin UniversityChangchunChina

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