期刊
GEOPHYSICAL JOURNAL INTERNATIONAL
卷 212, 期 2, 页码 1072-1097出版社
OXFORD UNIV PRESS
DOI: 10.1093/gji/ggx422
关键词
Image processing; Inverse theory; Time-series analysis
资金
- National Natural Science Foundation of China [61401307]
- Hebei Province Foundation of Returned oversea scholars [CL201707]
- Hebei Province Project of Science and Technology R D [11213565]
- Hebei Province Natural Science Foundation [E2016202341]
We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.
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