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2019 Separation and recovery of geophysical signals based on the Kalman filter with GRACE gravity data

发布时间:2019-03-13          来源:           浏览次数:

Xiaolong Wang1, Zhicai Luo2,3,*, Bo Zhong1,4,Yihao Wu5,6, Zhengkai Huang7, Hao Zhou2, and Qiong Li2

1 School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

2 MOE Key Laboratory of Fundamental Physical Quantities Measurement, Hubei Key Laboratory of Gravitation and Quantum Physics, Institute of Geophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China

3 Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

4 Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, China

5 School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China

6 State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China

7 School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China

Abstract: Monthly gravitational field solutions as spherical harmonic coefficients produced by the GRACE satellite mission require post-processing to reduce the effects of shortwave-length noises and north–south stripe errors. However, the spatial smoothing and de-striping filter commonly used in the post-processing step will either reduce spatial resolution or remove short-wavelength features of geophysical signals, mainly at high latitudes. Here, by using prior covariance information that reflects the spatial and temporal features of the geophysical signals and the correlated errors derived from the synthetic model, together with the covariance matrix of the formal errors for the monthly gravity spherical harmonic coefficients, we apply the Kalman filter to separate the geophysical signal from GRACE Level-2 data and simultaneously to estimate the correlated errors. By increasing the number of observations, the iterative process is applied to update the state vector and covariance in the Kalman filter because the prior information is not accurate. Due to the inevitable truncation error, multiple gridded-gain factors method considering different temporal frequencies has been developed to recover the geophysical signal. The results show that the Kalman filter can reduce the high-frequency noises and correlated errors remarkably. When compared with the commonly used filter, no spatial filter (such as Gaussian filter) is used in the Kalman filter. Therefore, the estimated signal preserves its natural resolution, and more detailed information is retained. It shows good consistency when compared with mascon solutions in both secular trend and annual amplitude.

Keywords: GRACE; Kalman filter; multiple gridded-gain factors


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