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Volume 29 Issue 1
Jan 2018
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Journal of Earth Science  > 2018  >  29(1): 230-236.
Min Hong, Desheng Shao, Tengfei Wu, Shuangxi Zhang, Yong Zhang, Lingli Wang, Xiaodong Qian. Short-impending Earthquake Anomaly Index Extraction of GNSS Continuous Observation Data in Yunnan, Southwestern China. Journal of Earth Science, 2018, 29(1): 230-236. doi: 10.1007/s12583-018-0826-0
Citation: Min Hong, Desheng Shao, Tengfei Wu, Shuangxi Zhang, Yong Zhang, Lingli Wang, Xiaodong Qian. Short-impending Earthquake Anomaly Index Extraction of GNSS Continuous Observation Data in Yunnan, Southwestern China. Journal of Earth Science, 2018, 29(1): 230-236. doi: 10.1007/s12583-018-0826-0
shu

Short-impending Earthquake Anomaly Index Extraction of GNSS Continuous Observation Data in Yunnan, Southwestern China

doi: 10.1007/s12583-018-0826-0
  • 1.

    Deformation Survey Center, Yunnan Earthquake Agency, Kunming 650041, China

  • 2.

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

  • 3.

    Key Laboratory of Environment and Geodesy, Ministry of Education, Wuhan 430079, China

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    Abstract
  • This paper presents a comprehensive area expansion prediction index method to apply GNSS for short-impending prediction of earthquakes. Based on continuous GNSS observation data from Yunnan Province, a displacement field was detected after data cycle-slip repair using precision data processing software and geophysical field effect model correction. The Yunnan area was divided into 56 grid cells for displacement field interpolation to obtain a more uniform displacement field and a strain field variation time series. The pre-earthquake response of each grid-cell expansion time series was evaluated and synthesized to extract a short-impending earthquake anomaly identification index. The results show that this index indicated occurrence times and hypocenter for earthquakes of magnitude M≥5. Fourteen earthquakes were predicted accurately, and there were five false reports. This index can therefore be used for the short-impending prediction of earthquakes.

     

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