Least-squares Kirchhoff migration with non-smooth regularisation strategy for subsurface imaging
文献类型：期刊论文
| 作者 | Hou, Jie1,2,3; Wang, Yanfei1,2,3 |
| 刊名 | EXPLORATION GEOPHYSICS
 |
| 出版日期 | 2018 |
| 卷号 | 49期号:6页码:793-802 |
| 关键词 | fast gradient algorithm least-squares migration non-smooth regularisation |
| ISSN号 | 0812-3985 |
| DOI | 10.1071/EG16132 |
| 英文摘要 | During the past several decades, many types of wave-equation migration methods arise for subsurface structure imaging. The classical Kirchhoff migration, however, is still widely adopted in the petroleum industry owing to its flexibility and computational efficiency. In constant density isotropic acoustic media, a basic assumption of the Kirchhoff migration is that every point of the subsurface model is supposed to be a diffractor which scatters wavefield energy to every direction, and hence collecting the scattered energy of all directions is the basic requirement for focusing the diffractor. Factors influencing the final image quality include incomplete data acquisition, multipathing from the surface to the imaging point, and insufficient illumination under complex overburden. All these factors can be theoretically taken into account in the migration weighting coefficient. However, computation of the weighting coefficient is hard work. In view of this difficulty, a fast regularising least-squares Kirchhoff migration algorithm is presented in this paper. It not only accounts for the irregular and incomplete data sampling (e.g. limited recording aperture, coarse sampling and acquisition gaps), but also compensates for the anomalous ray coverage and multipathing problem except for the shadow zone in the media. For the purpose of attenuating migration artefacts and providing a clear and accurate image of subsurface reflectivity, regularisation strategies are applied. The classical regularisation strategy may easily lead to over-regularisation or insufficient regularisation; we try to balance these two effects in this paper. The method is called the hybrid regularisation which incorporates smoothing and non-smoothing scale operators. The algorithm is implemented using a fast gradient decent solution method based on the Rayleigh quotient being used. Numerical experiments show that this hybrid regularisation method is powerful in handling the sparsity and smoothness of the model parameters. |
| WOS关键词 | RAY+BORN MIGRATION/INVERSION ; INVERSION ; REFLECTIVITY ; RETRIEVAL |
| 资助项目 | National Natural Science Foundation of China[41325016] ; National Natural Science Foundation of China[91630202] ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation |
| WOS研究方向 | Geochemistry & Geophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000451333400001 |
| 出版者 | CSIRO PUBLISHING |
| 资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation ; Collaboration Project of Seismic Imaging in Elastic Medium of the China National Petroleum Corporation |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/89607]  |
| 专题 | 地质与地球物理研究所_中国科学院油气资源研究重点实验室 |
| 通讯作者 | Wang, Yanfei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hou, Jie,Wang, Yanfei. Least-squares Kirchhoff migration with non-smooth regularisation strategy for subsurface imaging[J]. EXPLORATION GEOPHYSICS,2018,49(6):793-802. |
| APA | Hou, Jie,&Wang, Yanfei.(2018).Least-squares Kirchhoff migration with non-smooth regularisation strategy for subsurface imaging.EXPLORATION GEOPHYSICS,49(6),793-802. |
| MLA | Hou, Jie,et al."Least-squares Kirchhoff migration with non-smooth regularisation strategy for subsurface imaging".EXPLORATION GEOPHYSICS 49.6(2018):793-802. |
入库方式： OAI收割
来源：地质与地球物理研究所
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