基于压缩感知的地震观测系统设计及数据重建
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中国石油化工股份有限公司胜利油田分公司物探研究院

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P631

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国家科技重大专项(2016ZX05006);中国石油化工股份有限公司胜利油田地震成像联合实验室


Seismic Geometry Design and Data Reconstruction Based on Compressed Sensing
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1.Geophysical Research Institute of Shengli Oilfield,Sinopec,Shandong Dongying 257022;2.China

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    摘要:

    在陆上油田,受地表障碍物限制,常规的基于规则采样理论的地震数据采集越来越难以实现,同时为了解决越来越复杂的地质问题,需要更密集的空间采样,造成地震勘探成本急剧上升。为了适应复杂的地表条件和节省勘探成本,本文研究基于压缩感知理论设计随机地震观测系统,利用高维空间低秩约束算法完成随机地震数据的高密度规则化重建,通过理论模型对方法进行了验证,结果表明在同样采样密度下,该方法能获得比规则采样更好的成像效果,为当前东部老油区的高效高密度地震勘探探索一条新途径。

    Abstract:

    In land oilfields, limited by surface obstacles, conventional seismic data acquisition based on regular sampling theory is becoming more and more difficult to achieve. At the same time, in order to solve more and more complex geological problems, spatial sampling is more and more dense, which resulting in rapid increase seismic exploration costs. In order to adapt to complex surface conditions and save exploration costs, seismic exploration method based on compressed sensing theory was used to investigate, it is concluded that design method about random seismic geometry and uses low-rank constraint algorithms in high-dimensional space to complete the high-density regularization of random seismic data.The effectiveness of this method was proved through theoretical models, The results show that with the same sampling density, this method can obtain better imaging results than regular sampling, and explore a new approach for high-efficiency and high-density seismic exploration in the current eastern old oilfields.

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崔庆辉,芮拥军,秦宁,等. 基于压缩感知的地震观测系统设计及数据重建[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-10-25
  • 最后修改日期:2022-05-04
  • 录用日期:2022-05-07
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