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EARTHQUAKE ›› 2021, Vol. 41 ›› Issue (4): 168-179.doi: 10.12196/j.issn.1000-3274.2021.04.013

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Comparative Analysis of the Prediction Efficiency between Water-tube and Vertical Pendulum Tiltmeters in Qianjiang Station

GONG Li-wen1,2, CHEN Li-juan1, LÜ Pin-ji3, ZHANG Yan3, GUO Wei-ying1, XIAO Jia-qiang1   

  1. 1. Chongqing Earthquake Agency, Chongqing 401147, China;
    2. Key Laboratory of Computational Geodynamics of Chinese Academy of Science, University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Hubei Earthquake Agency, Hubei Wuhan 430071, China
  • Received:2019-11-22 Revised:2020-05-15 Online:2021-10-31 Published:2022-03-25

Abstract: Both the water-tube and vertical pendulum tiltmeters are deformation instruments for observing the tilt change of the ground to capture the earthquake precursor information. However, due to their different working principle and observation frequency band, there are many differences in the observations and abnormal patterns. Therefore, it’s necessary to evaluate the effectiveness of the abnormalities observed by different instruments. In this paper, the abnormal automatic processing software is used to extract the common anomalies such as trend transition, rate change, annual change and tidal factor of the Qianjiang station tiltmeters, and using R-value scores for different anomalies combined with regional earthquakes to assess the predictive effectiveness of different instruments. The results show that the NS component of the vertical pendulum has the best forecasting performance on the trend turning and the M2 tidal factor, and the EW components of the vertical pendulum and the water-tube have better performance in the annual change and rate change respectively, and the north-south component of the water-tube has the worst performance, which may be related to its frequent instrument failures. In addition, the seismic distribution corresponding to the anomalies of different observation items has a certain regularity.

Key words: Tilt observation, Precursor anomaly, R-value evaluation, Prediction efficiency

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