| 基于稀疏约束反演的三维混采数据分离 |
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| 引用本文： | 魏亚杰,张盼,许卓.基于稀疏约束反演的三维混采数据分离[J].地球物理学报,2019,62(10):4000-4009. |
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| 作者姓名： | 魏亚杰 张盼 许卓 |
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| 作者单位： | 1. 吉林大学地球探测科学与技术学院, 长春 130026;2. 河北地质大学, 石家庄 050031 |
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| 基金项目： | 国家高技术研究发展"863"计划重大项目"深部矿产资源探测技术"第05课题（2014AA06A605）和河北省自然科学基金青年基金项目（D2019403063）联合资助. |
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| 摘 要： | 混合震源采集技术相对于传统的地震数据采集，在极大提高采集效率的同时引入了混叠噪声，很大程度上影响了成像结果的精度.二维混采数据中，我们通常利用混叠噪声在非共炮域呈非相干分布这一特点来压制混叠噪声，从而实现混合震源数据分离.相对于二维混采数据，三维混采数据具有数据量巨大，构建混合震源算子困难，混合度的增加引入了高强度混叠噪声的特点.针对上述问题，本文采用稀疏约束反演方法在Radon域实现混采数据分离，混叠噪声强度比较大的情况下，稀疏约束反演方法能够得到更高精度的分离结果；利用震源激发的GPS时间通过长记录的方式在共接收点道集对上一次迭代分离结果做混合、伪分离，实现了单个共接收点道集自身混合、伪分离，避免了对整个数据做运算，同时不需要构建混合震源算子.通过模拟数据和实际数据计算来验证上述方法的适用性.
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| 关 键 词： | 稀疏约束反演 三维 混采数据分离 混叠噪声 GPS时间 |
| 收稿时间： | 2018-08-13 |
Separation of 3D blending seismic data based on sparse constrained inversion |
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| WEI YaJie,ZHANG Pan,XU Zhuo.Separation of 3D blending seismic data based on sparse constrained inversion[J].Chinese Journal of Geophysics,2019,62(10):4000-4009. |
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| Authors: | WEI YaJie ZHANG Pan XU Zhuo |
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| Institution: | 1. College of Geo-exploration Sciences and Technology, Jilin University, Changchun 130026, China;2. Hebei GEO University, Shijiazhuang 050031, China |
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| Abstract: | The simultaneous source acquisition technology can greatly improve the sampling efficiency. However, compared with the traditional acquisition technology, it may also lead to blended noise which reduces the imaging accuracy. For the 2-D blended data, we usually suppress such noise based on its incoherence in the non-common shot domain. While 3-D blended data contains more information, it has more strongly blended noise and makes it more difficult to construct the blending source operator. To solve these two problems, this paper proposes to separate the 3-D blended data in the Radon domain with sparse constrained inversion which can get higher precision of separation results. Using the GPS time excited by the source to blending and pseudo-deblending the results of the last iterative separation at the common receiver point gather by a long record can process the blending data one receiver by one receiver iteratively rather than the whole data. Such a method does not need to the construct the deblending operator. Tests on synthetic and measured data have proved the feasibility of this method. |
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| Keywords: | Sparse constraint inversion 3-D Seismic data deblending Blending noise GPS time |
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