期刊
GEOPHYSICAL JOURNAL INTERNATIONAL
卷 201, 期 2, 页码 1182-1194出版社
OXFORD UNIV PRESS
DOI: 10.1093/gji/ggv072
关键词
Fourier analysis; Wavelet transform; Inverse theory; Spatial analysis
资金
- National Natural Science Foundation of China [U1262207]
- Key State Science and Technology Project [2011ZX05023-005-005, 2011ZX05019-006]
- WTOPI Research Consortium at the University of California, Santa Cruz
- China Scholarship Council
Interpolation and random noise removal is a pre-requisite for multichannel techniques because the irregularity and random noise in observed data can affect their performances. Projection Onto Convex Sets (POCS) method can better handle seismic data interpolation if the data's signal-to-noise ratio (SNR) is high, while it has difficulty in noisy situations because it inserts the noisy observed seismic data in each iteration. Weighted POCS method can weaken the noise effects, while the performance is affected by the choice of weight factors and is still unsatisfactory. Thus, a new weighted POCS method is derived through the Iterative Hard Threshold (IHT) view, and in order to eliminate random noise, a new adaptive method is proposed to achieve simultaneous seismic data interpolation and denoising based on dreamlet transform. Performances of the POCS method, the weighted POCS method and the proposed method are compared in simultaneous seismic data interpolation and denoising which demonstrate the validity of the proposed method. The recovered SNRs confirm that the proposed adaptive method is the most effective among the three methods. Numerical examples on synthetic and real data demonstrate the validity of the proposed adaptive method.
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