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张掖5.0级地震前区域地壳变形异常特征分析
马海萍, 王谦, 张博, 武善艺, 王朋涛, 窦喜英, 李敏娟
甘肃省地震局
摘要:
为了研究2019年张掖5.0级地震前震中周边的地壳变形运动特征,根据CMONOC的GPS观测资料,通过GPS速度场分析、站间基线时间序列分析和震中区域的应变时间序列分析,探讨了震前的地壳变形特征。研究结果表明,震中周边的基线方位角时间序列反映了2017年以前祁连山北缘断裂应力场能量处于持续积累的状态,2017年后的方位角趋势转缓说明发震断裂带两侧的应力积累达到了一定的程度。张掖5.0地震前震中区域的第一剪应变和EW向线应变2017年后起出现明显的转平的现象,面收缩率2016年后逐渐减弱,表明震中区震前三年年存在明显的形变亏损的迹象,反映了该区域在震前积累了一定程度的应变能。研究结果可为认识此次地震的孕震过程和机理提供基础性研究资料。
关键词:  张掖5.0级地震  GPS基准站  基线方位角时间序列  应变时间序列
DOI:
分类号:
基金项目:地震星火计划 (XH20057),甘肃省地震科技发展基金 (2016M02, 2017IESLZ05), 国家自然基金 (51408567, 41304048).
Earthquake Research in ChinaVolume *, Number *, 20*
Ma Haiping, Wang Qian, Zhang Bo, Wu Shanyi, Wang Pengtao, Dou Xiying, Li Minjuan
Earthquake Administration of Gansu Province
Abstract:
To study the crustal movement around the epicenter before the Zhangye M5.0 earthquake in 2019, the characteristics of crustal deformation before the earthquake were discussed through the GPS velocity field analysis based on the GPS observation data of CMONOC. The baseline time series between two GPS continuous stations and the strain time series of an area among several stations were analyzed in the epicenter area. The results show that the time series of baseline azimuth around the epicenter reflects that the energy of the fault in the northern margin of Qilian Mountain is accumulated continuously before 2017. In addition, the trend of azimuth turns to slow down after 2017, indicating the stress accumulation on both sides of the seismogenic fault zone has reached a certain degree. The first shear strain and EW direction linear strain in the epicentral area of Zhangye 5.0 earthquake have been flattened obviously since 2017, and the surface strain rate weakened gradually after 2016. It is illustrated that there was an obvious deformation loss at the epicentral region in the three years before the earthquake, which reflects that a certain degree of strain energy is accumulated in the area before the earthquake.
Key words:  Zhangye M5.0 earthquake  GPS reference station  Velocity field  Baseline azimuth time-series  Strain time series