A fast joint seismic data reconstruction by sparsity-promoting inversion
文献类型：期刊论文
| 作者 | Bai, Lanshu1,2; Lu, Huiyi1; Liu, Yike1; Khan, Majid1,2 |
| 刊名 | GEOPHYSICAL PROSPECTING
 |
| 出版日期 | 2017-07-01 |
| 卷号 | 65期号:4页码:926-940 |
| ISSN号 | 0016-8025 |
| 关键词 | Seismic Data Reconstruction Sparsity Inversion Curvelet Transform |
| DOI | 10.1111/1365-2478.12455 |
| 文献子类 | Article |
| 英文摘要 | Seismic field data are often irregularly or coarsely sampled in space due to acquisition limits. However, complete and regular data need to be acquired in most conventional seismic processing and imaging algorithms. We have developed a fast joint curvelet-domain seismic data reconstruction method by sparsity-promoting inversion based on compressive sensing. We have made an attempt to seek a sparse representation of incomplete seismic data by curvelet coefficients and solve sparsity-promoting problems through an iterative thresholding process to reconstruct the missing data. In conventional iterative thresholding algorithms, the updated reconstruction result of each iteration is obtained by adding the gradient to the previous result and thresholding it. The algorithm is stable and accurate but always requires sufficient iterations. The linearised Bregman method can accelerate the convergence by replacing the previous result with that before thresholding, thus promoting the effective coefficients added to the result. The method is faster than conventional one, but it can cause artefacts near the missing traces while reconstructing small-amplitude coefficients because some coefficients in the unthresholded results wrongly represent the residual of the data. The key process in the joint curvelet-domain reconstruction method is that we use both the previous results of the conventional method and the linearised Bregman method to stabilise the reconstruction quality and accelerate the recovery for a while. The acceleration rate is controlled through weighting to adjust the contribution of the acceleration term and the stable term. A fierce acceleration could be performed for the recovery of comparatively small gaps, whereas a mild acceleration is more appropriate when the incomplete data has a large gap of high-amplitude events. Finally, we carry out a fast and stable recovery using the trade-off algorithm. Synthetic and field data tests verified that the joint curvelet-domain reconstruction method can effectively and quickly reconstruct seismic data with missing traces. |
| WOS关键词 | CONTINUOUS CURVELET TRANSFORM ; ORTHOGONAL MATCHING PURSUIT ; SIGNAL RECOVERY ; ALGORITHMS ; FRAMES |
| WOS研究方向 | Geochemistry & Geophysics |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:000403017400003 |
| 资助机构 | National Natural Science Foundation of China(41430321 ; Strategic Priority Research Program of Chinese Academy of Science(XDB10050300) ; 41374138) ; National Natural Science Foundation of China(41430321 ; Strategic Priority Research Program of Chinese Academy of Science(XDB10050300) ; 41374138) ; National Natural Science Foundation of China(41430321 ; Strategic Priority Research Program of Chinese Academy of Science(XDB10050300) ; 41374138) ; National Natural Science Foundation of China(41430321 ; Strategic Priority Research Program of Chinese Academy of Science(XDB10050300) ; 41374138) |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/52856]  |
| 专题 | 地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室 |
| 通讯作者 | Bai, Lanshu |
| 作者单位 | 1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bai, Lanshu,Lu, Huiyi,Liu, Yike,et al. A fast joint seismic data reconstruction by sparsity-promoting inversion[J]. GEOPHYSICAL PROSPECTING,2017,65(4):926-940. |
| APA | Bai, Lanshu,Lu, Huiyi,Liu, Yike,&Khan, Majid.(2017).A fast joint seismic data reconstruction by sparsity-promoting inversion.GEOPHYSICAL PROSPECTING,65(4),926-940. |
| MLA | Bai, Lanshu,et al."A fast joint seismic data reconstruction by sparsity-promoting inversion".GEOPHYSICAL PROSPECTING 65.4(2017):926-940. |
入库方式： OAI收割
来源：地质与地球物理研究所
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