Article Details

Seismic data restoration based on the Grassmannian rank-one update subspace estimation method

Oleh   Yatong Zhou [-]
Kontributor / Dosen Pembimbing : Chungying Han
Jenis Koleksi : Jurnal elektronik
Penerbit : Lain-lain
Fakultas :
Subjek :
Kata Kunci : Reconstruction, Seismic data processing, Noise, Subspace tracking
Sumber : Journal of Applied Geophysics 159 (2018) 731–741, https://www.sciencedirect.com/science/journal/09269851
Staf Input/Edit : Devi Septia Nurul  
File : 1 file
Tanggal Input : 2019-02-11 16:20:29

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2018_EJRNL_PP_YATONG_ZHOU_1.pdf


A new low-rank based framework has been proposed for simultaneous noise attenuation and data reconstruction, which uses an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The online subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analyzing incremental gradient descent on the Grassmannian manifold of subspaces. When the multi-dimensional seismic data is 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 proposed subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional singular value decomposition (SVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed reconstruction method can obtain better performance. More specifically, the proposed method outperforms the singular spectrum analysis (SSA) 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