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A Network Inversion Filter combining GNSS and InSAR for tectonic slip modeling

机译:结合GNSS和InSAR的网络反演滤波器用于构造滑动建模。

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Studies of the earthquake cycle benefit from long-term time-dependent slip modeling, as it can be a powerful means to improve our understanding on the interaction of earthquake cycle processes such as interseismic, coseismic, post seismic, and aseismic slip. Observations from InterferometricSynthetic Aperture Radar (InSAR) allow us to model slip at depth with a higher spatial resolution than when using Global Navigation Satellite Systems (GNSS) alone. While the temporal resolution of InSAR has typically been limited, the recent fleet of SAR satellites including Sentinel-1, COSMO-SkyMED, and RADARSAT-2 permits the use of InSAR for time-dependent slip modeling at intervals of a few days when combined. With the vast amount of SAR data available, simultaneous data inversion of all epochs becomes challenging. Here we expanded the original network inversion filter to include InSAR observations of surface displacements in addition to GNSS. In the Network Inversion Filter (NIF) framework, geodetic observations are limited to those of a given epoch, with a stochastic model describing slip evolution over time. The combination of the Kalman forward filtering and backward smoothing allows all geodetic observations to constrain the complete observation period. Combining GNSS and InSAR allows modeling of time-dependent slip at unprecedented spatial resolution. We validate the approach with a simulation of the 2006 Guerrero slow slip event. We highlight the importance of including InSAR covariance information and demonstrate that InSAR provides an additional constraint on the spatial extent of the slow slip.
机译:地震周期的研究得益于长期的时变滑动模型,因为它可以作为一种强有力的手段来增进我们对地震周期过程(例如,间震,同震,后地震和抗震滑动)相互作用的理解。与单独使用全球导航卫星系统(GNSS)相比,干涉合成孔径雷达(InSAR)的观测使我们能够以更高的空间分辨率对深度滑动进行建模。尽管InSAR的时间分辨率通常受到限制,但最近的SAR卫星群(包括Sentinel-1,COSMO-SkyMED和RADARSAT-2)允许将InSAR用于结合时间的滑动建模,间隔为几天。有了大量的SAR数据,所有时期的同时数据反演变得充满挑战。在这里,我们扩展了原始的网络反演滤波器,除了GNSS之外,还包括InSAR观测的表面位移。在网络反转过滤器(NIF)框架中,大地观测仅限于给定纪元的观测,其随​​机模型描述了随时间推移的滑移演化。卡尔曼正向滤波和向后平滑的组合使所有大地观测都可以约束整个观测周期。将GNSS和InSAR结合使用,可以以空前的空间分辨率对随时间变化的滑模进行建模。我们通过模拟2006年Guerrero慢滑事件来验证该方法。我们强调了包含InSAR协方差信息的重要性,并证明了InSAR对慢滑的空间范围提供了额外的约束。

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