High precision coseismic deformation monitoring method based on time-series InSAR analysis
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摘要: 合成孔径雷达干涉测量（InSAR）技术凭借全天时、全天候对地监测、高空间分辨率等特点，成为监测地表形变的重要手段，并广泛地应用到地震形变监测领域. 然而同震形变监测中最常用的D-InSAR技术在水域和植被覆盖严重等区域中容易受到时空失相关的影响，导致获取的同震形变场会受到严重的污染，此外还包含有大气延迟误差. 本文提出基于时序InSAR分析的高精度同震形变监测方法获取地震同震形变结果，主要是通过选择合适的干涉对和选择稳定点两步来提高形变场精度. 凭借充足的Sentinel-1A/B卫星SAR数据的支撑，利用大量震前和震后影像生成众多干涉图，按照一定标准挑选受误差影响较小的干涉图进行研究，减少大气延迟误差造成的影响；同时对震前影像的幅度图进行统计分析，从幅度值、相干性和幅度离散指数等方面设置阈值选择稳定点目标，削弱噪声干扰，提高形变场精度. 本文以2018年中国台湾花莲MW6.4地震为例，详细地介绍了高精度同震形变监测方法的数据处理流程，并与传统D-InSAR方法的结果进行了精度比较，结果表明本文方法能削弱形变场中的噪声误差，提高同震形变的信噪比. 应用本文方法获取了14个不同震级和位置的地震形变，结果表明通过选择稳定点的方式能提高形变场精度，且对于获取不同地震的同震形变场具有普遍适用性.Abstract: Interferometry Synthetic Aperture Radar (InSAR) technology has become an important tool for monitoring surface deformation with its all-day, all-weather ground monitoring and high spatial resolution, and has been widely applied to seismic deformation monitoring. Currently, the most commonly used technique for coseismic deformation monitoring is differential InSAR (D-InSAR). However, the traditional D-InSAR is susceptible to spatial and temporal uncorrelation in areas such as waters and densely vegetated areas, resulting in serious contamination of the coseismic deformation field. In addition, the seismic deformation field sometimes contains obvious atmospheric delay that can affect source parameter inversions. Therefore, improving the quality of coseismic deformation is of great significance for future seismic deformation monitoring and parameter inversion. The multi-temporal InSAR (MT-InSAR) technique, which is widely used in inter-seismic and post-seismic deformation monitoring, is able to suppress the effects of spatiotemporal decorrelation and atmospheric noise. In this paper, we propose a high-precision coseismic deformation monitoring method based on time-series InSAR analysis to obtain high-precision coseismic deformation results. The accuracy of coseismic deformation field is mainly improved by selecting appropriate interferograms and selecting stable points. With the support of sufficient Sentinel-1A/B satellite SAR data, numerous interferograms were generated using a large number of pre- and post-earthquake images. Interferograms that are less affected by errors are selected for study according to certain criteria to reduce the impact caused by atmospheric delay errors. At the same time, setting threshold to select stable point target to improve the accuracy of deformation field. Taking the 2018 Hualian MW6.4 earthquake in Taiwan China as an example, the data processing flow of high-precision coseismic deformation monitoring method is introduced in detail. Compared with the results of the traditional D-InSAR method, the proposed method can reduce the noise error and improve the signal-to-noise ratio of coseismic deformation. The high-precision coseismic deformation monitoring method is applied to obtain the seismic deformation of 14 different magnitudes and locations. The deformation results show that the method can improve the accuracy of deformation field by selecting stable points, and it is generally applicable.
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图 1 高精度同震形变监测方法获得同震形变场的流程图. 整个过程包括输入、处理和输出三个部分，其中处理部分包括差分干涉图选择和稳定点选择两步
Figure 1. Flowchart of obtaining coseismic deformation by the proposed high-precision coseismic deformation monitoring method. The whole process includes three parts: input, processing and output. The processing part includes differential interferograms choosing and stable point selection
图 2 地震案例研究位置分布图. 红色沙滩球是USGS给出的震源机制解，蓝色方框是每个地震对应的Sentinel-1A/B影像升降轨覆盖范围
Figure 2. The location distribution map of the selected earthquake. The red beach ball is the focal mechanism solution determined by USGS, and the blue box is coverage of the Sentinel-1A/B ascending and descending image for each earthquake
图 3 2018年花莲地震升轨和降轨干涉图网，升轨和降轨各有33对干涉图，红色实线表示2018年地震发生的时刻
Figure 3. Interferogram network of the ascending and descending tracks of the 2018 Hualian earthquake. 33 pairs of interferograms for the ascending and descending tracks, and the red line indicates the occurrence time of the 2018 earthquake
图 4 2018年花莲地震升轨33幅干涉图结果. 每个干涉图非变形区域的STD显示在子图的右下角，红色方框内是STD最小的干涉对
Figure 4. The 33 ascending interferogram pairs of the Hualien earthquake. The STD of the non-deformation area map of each interferogram is shown in the bottom right corner of the subfigure. The red box is the smallest interferogram pair of STD
图 5 2018年花莲地震降轨33幅干涉图结果. 每个干涉图非变形区域的STD显示在子图的右下角，红色方框内是STD最小的干涉对
Figure 5. The 33 descending interferogram pairs of the Hualien earthquake. The STD of the non-deformation area map of each interferogram is shown in the bottom right corner of the subfigure. The red box is the smallest interferogram pair of STD
图 6 Sentinel-1影像获取的2018年花莲地震的同震形变场. （a, b）用本文高精度同震形变监测方法得到的升轨和降轨形变结果；（e, f）传统D-InSAR获取的升轨和降轨结果；（c, d, g, h）分别对应图（a）中选取的A、B、C、D四个区域的光学图
Figure 6. The coseismic deformation fields of the 2018 Hualien earthquake derived from Sentinel-1 A/B images. (a, b) The ascending and descending interferograms obtained from high-precision coseismic deformation monitoring method, respectively; (e, f) The ascending and descending coseismic interferograms obtained by the classic D-InSAR, respectively; (c, d, g, h) Correspond to the optical images of the areas A, B, C and D selected in (a), respectively
图 7 实验所用的14个地震案例的位置分布和同震形变结果
Figure 7. Location distribution and coseismic deformation results of 14 earthquake cases
图 8 实验中14个地震案例升轨和降轨形变场的最大形变量及误差标准差
Figure 8. The maximum deformation and error standard deviations in the ascending and descending deformation fields of 14 seismic cases
表 1 实验分析所用地震参数信息
Table 1. Seismic parameter information for experimental analysis
编号 日期 参考位置 经度/°E 纬度/°N 深度/km 节面Ⅰ 矩震级 走向/(°) 倾角/(°) 滑动角/(°) 1 2015-04-25 西藏定日县 87.3 28.4 20 - - - 5.9 2 2015-07-03 新疆皮山县 78.2 37.6 10 317 69 102 6.5 3 2016-01-21 青海门源县 101.62 37.68 10 337 41 103 6.4 4 2016-11-25 新疆阿克陶县 74.04 39.27 10 199 84 14 6.7 5 2018-02-06 台湾花莲县 121.71 24.13 11 209 73 22 6.4 6 2018-12-24 西藏谢通门县 87.64 30.32 8 206 42 −66 5.8 7 2020-01-19 新疆伽师县 77.21 39.83 16 221 20 72 6.4 8 2020-03-20 西藏定日县 87.42 28.63 10 180 42 −77 5.9 9 2020-06-26 新疆于田县 82.33 35.73 10 24 42 −108 6.3 10 2020-07-23 西藏尼玛县 86.81 33.19 10 203 29 −88 6.6 11 2021-03-19 西藏比如县 92.74 31.94 10 17 37 −113 6.1 12 2021-03-30 西藏双湖县 87.68 34.38 10 173 55 −129 5.8 13 2021-05-21 云南漾濞县 99.87 25.67 8 135 82 −165 6.4 14 2021-05-22 青海玛多县 98.34 34.59 17 92 67 −40 7.4 
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表 2 2018年花莲地震Sentinel-1A/B卫星影像信息
Table 2. Sentinel-1A/B satellite image information of the 2018 Hualien earthquake
卫星 轨道 震前影像时间 震后影像时间 干涉图数量 Sentinel-
1A/B升轨 2017-10-06 2017-10-18
2017-10-30 2017-11-11
2017-11-23 2017-12-05
2017-12-17 2017-12-29
2018-01-10 2018-01-22
2018-02-032018-02-09
2018-02-15
2018-02-2733 降轨 2017-09-14 2017-09-26
2017-10-08 2017-10-20
2017-11-13 2017-11-25
2017-12-07 2017-12-19
2017-12-31 2018-01-12
2018-02-052018-02-11
2018-02-17
2018-03-0133
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表 3 本文方法和传统D-InSAR方法所得形变场的精度比较
Table 3. The accuracy comparison of the deformation field obtained by the proposed method and the traditional D-InSAR method
轨道 方法 STD/cm A B C D 升轨 本文方法 0.91 0.83 0.89 0.52 传统D-InSAR方法 1.05 0.84 1.01 0.56 降轨 本文方法 0.86 0.72 1.08 0.80 传统D-InSAR方法 1.34 0.75 1.2 0.88
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