Sparse DDK

Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products

Qian, N. (TU Delft Physical and Space Geodesy; China University of Mining and Technology)
Chang, Guobin (China University of Mining and Technology)
Ditmar, P.G. (TU Delft Physical and Space Geodesy)
Gao, Jingxiang (China University of Mining and Technology)
Wei, Zhengqiang (China University of Mining and Technology)

2022

High-frequency and correlated noise filtering is one of the important preprocessing steps for GRACE level-2 products before calculating mass anomaly. Decorrelation and denoising kernel (DDK) filters are usually considered as such optimal filters to solve this problem. In this work, a sparse DDK filter is proposed. This is achieved by replacing Tikhonov regularization in traditional DDK filters with weighted L1 norm regularization. The proposed sparse DDK filter adopts a time-varying error covariance matrix, while the equivalent signal covariance matrix is adaptively determined by the Gravity Recovery and Climate Experiment (GRACE) monthly solution. The covariance matrix of the sparse DDK filtered solution is also developed from the Bayesian and error-propagation perspectives, respectively. Furthermore, we also compare and discuss the properties of different filters. The proposed sparse DDK has all the advantages of traditional filters, such as time-varying, location inhomogeneity, and anisotropy, etc. In addition, the filtered solution is sparse; that is, some high-degree and high-order terms are strictly zeros. This sparsity is beneficial in the following sense: high-degree and high-order sparsity mean that the dominating noise in high-degree and high-order terms is completely suppressed, at a slight cost that the tiny signals of these terms are also discarded. The Center for Space Research (CSR) GRACE monthly solutions and their error covariance matrices, from January 2004 to December 2010, are used to test the performance of the proposed sparse DDK filter. The results show that the sparse DDK can effectively decorrelate and denoise these data.

DDK filter
GRACE
L1-norm regularization
mass anomaly

http://resolver.tudelft.nl/uuid:38875915-078b-4ae6-a479-78d61514e8f9

https://doi.org/10.3390/rs14122810

2072-4292

Remote Sensing, 14 (12)

Institutional Repository

journal article

© 2022 N. Qian, Guobin Chang, P.G. Ditmar, Jingxiang Gao, Zhengqiang Wei