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摘要: 利用GNSS网络位移时空序列，基于弹性位错理论，构建了断层滑移时空分布的动态卡尔曼滤波反演模型。考虑断层面的非均匀滑动，将断层面细分为多个子断层，获取了较精细的滑移空间分布，并顾及了先验信息和拉普拉斯平滑约束。鉴于断层滑移引起的地表形变具有高空间相关性的特点，利用整个GNSS观测网络数据一起参与反演，有效分离了空间不相关的噪声。实验结果表明，当断层形变位移量和噪声水平相当，且点位分布间隔沿走向和倾向至少与子断层长宽等同时，均能反演得到正确的断层滑移时空分布。若信噪比不变，测站分布密度继续增大时，对反演效果提高并不显著，但能够容忍较低信噪比的观测数据。Abstract: According to the OKADA fault dislocation theory, the model for spatio-temporal inversion of fault slips is built based on Kalman filtering using the GNSS displacement time-space series. To acquire more subtle distribution of a fault slip, the fault is divided into many subfaults. The priori information and the Laplacian smoothing constraint is taken into account. According to the high spatial coherence of surface deformation from fault slip,the spatially-uncorrelated noise is separated effectively by a Kalman filtering inversion of the whole GNSS network. Simulation experiments indicate that since the displacement from the fault deformation is equivalent to the noises level, and GNSS point distribution intervals in strike and dip are equivalent to the length and width of the subfault at least, then the spatiotemporal distribution of the fault slip can be obtained accurately. When the distribution density of the observation stations continues to increase, improvements of the inversion effect are not apparent. However, the high distribution density of stations is very helpful to improve the Signal Noise Ratio(SNR) of the inversion.
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Keywords:
- GNSS /
- dynamic Kalman filter /
- fault slip /
- inversion
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图 3 模拟噪声与合成地表位移序列(试验1)
Figure 3. Simulation Noises and Synthetic Displacement Series(Test 1)
表 1 断层参数
Table 1 Fault Parameters
下边缘中心位置(N,E)/km 方位角/(°) 长/km 宽/km 下边缘深/km 倾角/(°) (50,50) 90 40 5 5 70 
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