李志才, 陈智, 武军郦, 周星, 张鸣之, 赵利江, 余博尧, 周佳, 张澍. 基于高频GNSS观测的甘肃积石山6.2级地震同震形变[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240004
引用本文: 李志才, 陈智, 武军郦, 周星, 张鸣之, 赵利江, 余博尧, 周佳, 张澍. 基于高频GNSS观测的甘肃积石山6.2级地震同震形变[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20240004
Li Zhicai, Chen Zhi, Wu Junli, Zhou Xing, Zhang Mingzhi, Zhao Lijiang, Yu Boyao, Zhou Jia, Zhang Shu. Co-seismic Deformation of the Jishishan 6.2 Earthquake in Gansu Province Based on High-Frequency GNSS Observation[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240004
Citation: Li Zhicai, Chen Zhi, Wu Junli, Zhou Xing, Zhang Mingzhi, Zhao Lijiang, Yu Boyao, Zhou Jia, Zhang Shu. Co-seismic Deformation of the Jishishan 6.2 Earthquake in Gansu Province Based on High-Frequency GNSS Observation[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240004

基于高频GNSS观测的甘肃积石山6.2级地震同震形变

Co-seismic Deformation of the Jishishan 6.2 Earthquake in Gansu Province Based on High-Frequency GNSS Observation

  • 摘要: 2023年12月18日发生的甘肃积石山6.2级地震,造成了重大的人员伤亡。收集了不同系统32个卫星导航定位基准站(continuous operational reference system,CORS)当天的高频全球导航卫星系统(global navigation satellite system,GNSS)观测数据,进行了高精度动态单历元数据处理。处理结果发现,震中附近50 km以内的高频GNSS可以监测到明显的同震形变波形,峰值变化最大达到50~60 mm;地震永久变形主要影响范围为距离震中30 km的区域,距离震中5 km的测站记录到东西向永久变形~13 mm,南北向变形~10 mm,以及~8 mm的垂向变形。采用自适应噪声完全集合经验模态分解方法对高频GNSS波形信号进行分解,可分离出明显的同震形变信号,最远可探测到距离震中100 km。采用模态分解方法可探测出更多的同震信号,这为利用丰富的CORS站资源监测地震的同震形变提供更多可行性。

     

    Abstract: Objectives: The aim of this study is to obtain a high-precision co-seismic deformation field of a 6.2 magnitude earthquake occurred in Jishishan County, Gansu Province, China from high frequency GNSS observation data, which will enable the real-time monitoring of deformations for earthquakes with a magnitude of 6 or higher using a massive network of continuously operating reference stations (CORS). Methods: This study employed the Pride 3.0 software developed by Wuhan University for high-precision dynamic processing of 32 CORS station with high- frequency data, obtaining co-seismic deformation time series for each station. For stations showing significant co-seismic responses in close proximity, dynamic solutions with large changes at the seismic moment were first excluded. The smoothed single-epoch results before and after the earthquake were retained. For stations with less obvious co-seismic responses, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method was used to detect high-frequency co-seismic signals. Results: The main range of permanent deformation caused by earthquakes is in the area 30km away from the epicenter. Stations 5km away from the epicenter recorded permanent deformation in the east-west direction of 13mm, north-south direction of 10mm, and vertical deformation of 8mm. Modal decomposition identified 13 stations with seismic responses in the east or north directions, including CORS stations LXJS, GUTI, LXYJ within a 40 km radius from the epicenter. It could detect the seismic deformation far from 100km better than the original GNSS results, which means this method could be used for waveform signal reconstruction, noise removal, and expanding the distance of CORS station monitoring for co-seismic signals. Conclusions: This study indicate that various types of CORS station equipment with different foundation can monitor earthquake signals. The CEEMDAN method for signal decomposition can detect co-seismic signals faraway, providing feasibility for monitoring seismic co-seismic deformation information using a large number of CORS station resources in the future.

     

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