1. 中国石油大学(北京)油气资源与探测国家重点实验室, 北京 102249;
2. 中国石油大学(北京)CNPC物探重点实验室, 北京 102249;
3. The University of Texas, Austin, Texas, US;
4. 东方地球物理公司大港物探处, 天津 300280
Irregularly sampled multi-source data deblending based on shaping regularization
Zu Shaohuan1,2, Zhou Hui1,2, Chen Yangkang3, Qu Shan1,2, Liu Renwu4, Chen Haolin4
1. State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China;
2. CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum(Beijing), Beijing 102249, China;
3. The University of Texas, Austin, Texas, US;
4. Dagang Department, BGP Inc., CNPC, Dagang, Tianjin 300280, China
Abstract：Due to the limitations of acquisition conditions and costs, multi-source data may be incomplete or irregularly sampled. We propose in this paper a method for deblending and interpolating multi-source data. By introducing Mask operators, we use regularly sampled data deblending in irregularly sampled multi-source data deblending and interpolation and present its iterative framework. We first transform irregularly sampled multi-source data into a sparse domain, then use a shaping regularization in the sparse domain to achieve both deblending and interpolation. Tests on synthetic and field data demonstrate the validity and effectiveness of the proposed method.
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