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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Acknowledgments
We would like to thank the Crustal Movement Monitoring Engineering Research Center for providing the GNSS continuous observation data. This study was supported by the National 973 Project of China (No. 2013CB733303), the National Natural Science Foundation of China (No. 41474093), the Key Natural Science Foundation of Hubei Province (No. 2014CFA110), the open fund of Key Laboratory of Geospace Environment and Geodesy, Ministry of Education (No. 15-02-07), the China Earthquake Administration’s Earthquake Science and Technology Spark Program (No. XH15037SX), Special fund of earthquake science and technology of Yunnan Earthquake Agency (No. 2017ZX02), and the Jiancheng Li Academician Workstation (No. 2015IC015). The final publication is available at Springer via https://doi.org/10.1007/s12583-018-0826-0.
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Hong, M., Shao, D., Wu, T. et al. Short-Impending Earthquake Anomaly Index Extraction of GNSS Continuous Observation Data in Yunnan, Southwestern China. J. Earth Sci. 29, 230–236 (2018). https://doi.org/10.1007/s12583-018-0826-0
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DOI: https://doi.org/10.1007/s12583-018-0826-0