| 一阶多次波聚焦变换成像 |
| |
| 引用本文： | 刘学建,刘伊克,胡昊,谢宋雷.一阶多次波聚焦变换成像[J].地球物理学报,2015,58(6):1985-1997. |
| |
| 作者姓名： | 刘学建 刘伊克 胡昊 谢宋雷 |
| |
| 作者单位： | 1. 中国科学院地质与地球物理研究所 中国科学院工程地质力学重点实验室, 北京 100029;
2. 中国科学院大学, 北京 100049;
3. 中国科学院南海海洋研究所, 广州 510301 |
| |
| 基金项目： | 国家自然科学基金项目(41374138),国家油气重大专项(2011ZX05008-006)和中国科学院战略性先导科技专项(B类)(XDB01020300)联合资助. |
| |
| 摘 要： | 将多次波转换成反射波并按传统反射波偏移算法成像,是多次波成像的一种方法.聚焦变换能准确的将多次波转换为纵向分辨率更高的新波场记录,其中一阶多次波转换为反射波.本文对聚焦变换提出了两点改进:1)提出局部聚焦变换,以减小存储量和计算量,增强该方法对检波点随炮点移动的采集数据的适应性;2)引入加权矩阵,理论上证明原始记录的炮点比检波点稀疏时,共检波点道集域的局部聚焦变换可以将多次波准确转换成炮点与检波点有相同采样频率的新波场记录.本文在第一个数值实验中对比了对包含反射波与多次波的原始记录做局部聚焦变换和直接对预测的多次波做局部聚焦变换两种方案,验证了第二种方案转换得到的波场记录信噪比更高且避免了第一个方案中切聚焦点这项比较繁杂的工作.第二个数值实验表明:在炮点采样较为稀疏时,该方法能有效的将一阶多次波转换成反射波;转换的反射波能提供更丰富的波场信息,成像结果更均衡、在局部有更高的信噪比,以及较高的纵向分辨率.
|
| 关 键 词： | 聚焦变换 多次波成像 多次波消除 |
| 收稿时间： | 2014-05-08 |
Focal transformation imaging of first-order multiples |
| |
| LIU Xue-Jian,LIU Yi-Ke,HU Hao,XIE Song-Lei.Focal transformation imaging of first-order multiples[J].Chinese Journal of Geophysics,2015,58(6):1985-1997. |
| |
| Authors: | LIU Xue-Jian LIU Yi-Ke HU Hao XIE Song-Lei |
| |
| Institution: | 1. Key Laboratory of Engineering Geomechanics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China |
| |
| Abstract: | Surface-related multiples penetrate into the subsurface several times and contain abundant reflection information of small angles. Compared with primaries, they sometimes can provide higher fold and better illumination for subsurface. Instead of trashing multiples as noises during the seismic data processing, nowadays, a lot of methods have been proposed to image multiples. Conventional migration methods have been modified for directly imaging multiples, e.g., Kirchhoff migration of multiples, wave equation migration of multiples, or reverse-time migration (RTM) of multiples. Alternatively, the linear two-step procedures can be utilized for imaging multiples. Primaries can be extracted from multiples based on seismic interferometry or focal transformation, and then imaged by utilizing conventional migration methods. However, most methods for imaging multiples would generate so many artifacts due to the undesired interactions between seismic-events (i.e. primaries and different-order multiples), which are hard to be attenuated and seriously pollute the true-image. Moreover, the least-squares based focal transformation can transform first-order multiples into primaries, second-order multiples into first-order multiples, etc., with few noises generated. Multiples focal-transformed from higher-order multiples can be eliminated by surface-related multiples elimination (SRME). And then, primaries focal-transformed from first-order multiples can be imaged by any conventional migration methods, and we call the procedures focal transformation imaging of first-order multiples.
The focal transformation is proposed by Berkhout and Verschuur, which is developed from SRME. Primaries are once subsurface response of sources, surface-related multiples include different-orders, and the raw data contain primaries and surface-related multiples. Focal transformation is manipulated with several matrices that contain the full data of mono-frequency, one column stores one shot gather or one common-receiver gather. Primaries need to be estimated prior to focal transformation, and the inverse of primaries matrix is used as focal operator. The full raw data will be transformed into the new wavefield records. In fact, the focal transformation is implemented with a stable form of least-squares sense. Primaries are focused around one point in the profile of zero-time and zero-offset, the so called focal-point; first-order multiples are transformed into primaries; second-order multiples are transformed into first-order multiples; etc. Focal transformation is a kind of least-squares transformation, which nearly doesn't generate noises. So, after muting the energies around focal point, SRME can be utilized to eliminate the multiples transformed from higher-order multiples, and transformed primaries are obtained. Alternatively, in order to utilize the information of multiples, we can directly implement the focal transformation of multiples that usually have been separated from primaries during the regular seismic data processing, and the muting work can be avoided. On the other hand, the focal transformation has the effect of deconvolution by utilizing the inverse of real source-signature matrix, so the transformed primaries have higher vertical resolution than the acquired primaries. However, when the receive array moves with the source position, the matrices will occupy large memory that are mostly wasted by off diagonal 0 elements. In this article, we put forward two improvements for focal transformation: (1) develop the local focal transformation for reducing storage and computation; (2) bring in the weighted matrix, and demonstrating that local focal transformation in common-receiver domain can transform multiples into new wavefield records retrieving the missing shot-gathers of acquired data. The local transformation is implemented specially for one shot gather or one common-receiver gather, so the local focal transformation has better adaptation to the acquired data whose traces move with corresponding source position. We introduce the diagonal matrix into the focal transformation. When source sampling rate is sparser than receiver sampling rate of acquired data and one column of the matrices stores one common-receiver gather, the diagonal elements of the weighted matrix are periodically 1 spaced by 0 diagonal elements. The focal transformation transforms multiples into new wavefield records where source sampling is the same with receiver sampling, when the source sampling is sparser than receiver sampling in the acquired data. The local focal transformation in common-receiver domain also can transform multiples into new wavefild records with denser source sampling, but the common-receiver gathers must be extracted from shot-gathers in advance.
In the first numerical test, two workflows of local focal transformation of row data and direct local focal transformation of predicted multiples are compared. They are both implemented in common-shot domain. We verify the second work flow will generate wavefield records with higher signal to noise ratio, and the laborious task for muting the energies around focal point is avoided. With the first workflow, a few parts of primaries are not focused around the focal point and leaked into the profile transformed from multiples. With the second workflow, the new wavefiled records directly transformed from multiples nearly do not have noises, and multiples transformed from higher multiples can be successfully eliminated by SRME. Obviously, the wavefield records transformed from multiples have higher vertical resolution than the acquired data. The second numerical test demonstratesthat when source sampling is relatively sparse, the local focal transformation in common-receiver domain can effectively transform first-order multiples into primaries; the transformed primaries include the missing shot-gathers of the acquired data; the imaging result of transformed primaries is more balanced, locally has higher signal-to-noise ratio, and shows slightly higher vertical resolution. The separated primaries and multiples are both rearranged into common-receiver gathers in advance; the zero traces in common-receiver gathers show the missing shot records; that every other trace in common-receiver gathers are zero represents the sparse source sampling, and alias artifacts can be clearly seen in the FK domain. The direct local transformation of multiples on common-receiver gathers can retrieve the missing shot records from multiples, and the alias artifacts in the FK domain are avoided. In the common-receiver domain, multiples transformed from higher-order multiples are eliminated by SRME, and the transformed primaries are rearranged back into the common-shot domain. The transformed primaries have wider amplitude spectrum than acquired primaries, which demonstrate focal transformation has the effect of deconvolution. The transformed primaries and acquired primaries are both migrated by RTM.
The focal transformation can transform first-order multiples into primaries with few noises, and it has the effect of deconvolution. The proposed local focal transformation is implemented specially for one shot or one common-receiver gather, so the local focal transformation can save computation and storage. The local focal transformation in common-receiver domain can retrieve the missing shot records of acquired data from multiples. When source sampling of acquired data is sparse, the RTM image of primaries transformed from first-order multiples is more balanced, locally has higher signal-to-noise ratio, and shows slightly higher vertical resolution. |
| |
| Keywords: | Focal transformation Imaging of multiples Multiples elimination |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《地球物理学报》浏览原始摘要信息 |
| 点击此处可从《地球物理学报》下载免费的PDF全文 |
|
