2022年泸定M6.8地震为近期川滇地区显著强震。本文基于Okubo平面矩形弹性位错理论,采用已有的由地震波反演获得的同震破裂模型,模拟研究了泸定M6.8地震产生的地表同震重力变化、垂直位移和水平位移。结果表明:①同震重力变化图像具有以发震断层为界,呈正、负四象限对称分布特征,与震前(2019年9月—2020年9月)实测重力变化图像和发震断层左旋走滑特征具有一致性,说明其孕震过程可用闭锁剪力模型来解释; ②在远场区域,同震垂直位移图像与重力变化图像类似,位错引起的介质密度变化效应大于地表垂直位移效应,而近场地表垂直位移效应大于介质密度变化效应,显示出负相关性; ③同震水平位移图像具有对称的四象限特征,与GNSS实测结果显示的变形特性一致。该结果可为地震前后重力、形变观测结果的解释提供依据,同时为强震孕育的机理研究提供线索,尤其为闭锁剪力孕震模式的进一步完善提供了新的实例依据。

The Luding M6.8 earthquake in 2022 is a critical earthquake in the recent Sichuan-Yunnan region. Based on the coseismic rupture model obtained from seismic wave and the Okubo planar rectangular elastic dislocation theory,we simulated the coseismic gravity changes,vertical and horizontal displacements caused by the Luding M6.8 earthquake. The results show that:① The coseismic gravity change image has a high and low four-quadrant symmetrical feature with the fault as the boundary,which is consistent with the pre-seismic(September 2019 to September 2020)measured gravity change image and the left-lateral strike-slip feature of the fault,indicating that the seismic precursor process is related with the lock in shearing force model. ② In the far-field region,the coseismic vertical displacement pattern exhibits similarity to the gravity anomaly pattern. The effect of density variation induced by fault slip surpasses the impact of surface vertical displacement in this context. In contrast,the surface vertical displacement in the near-field exerts a more pronounced influence compared to the density variation effect,demonstrating a negative correlation between them. ③ The coseismic horizontal deformation image has a symmetrical four-quadrant feature,which is consistent with the deformation characteristics by GNSS measurements and InSAR results. These results provide a basis for explaining the gravity and deformation observation results before and after the earthquake,and help us to understand the mechanism of strong earthquake generation,especially for the further improvement of the locked shear model.

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国家自然科学基金(42374105、41774015)、国家重点研发课题(2018YFE0206100)共同资助