北京大学学报自然科学版 ›› 2021, Vol. 57 ›› Issue (6): 1101-1107.DOI: 10.13209/j.0479-8023.2021.083

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降维虚同相轴法压制陆地层间多次波

谢飞1, 安圣培1, 朱成宏1, 刘嘉辉2, 胡天跃2,†   

  1. 1. 中国石化石油勘探开发研究院, 北京100083 2. 北京大学地球与空间科学学院, 北京 100871
  • 收稿日期:2020-12-02 修回日期:2021-01-26 出版日期:2021-11-20 发布日期:2021-11-20
  • 通讯作者: 胡天跃, E-mail: tianyue(at)pku.edu.cn
  • 基金资助:
    国家重点研发计划(2018YFA0702503)和国家自然科学基金(41674122)资助

Dimensionality Reduced Virtual Event Method to Suppress Internal Multiples for Land Seismic Data

XIE Fei1, AN Shengpei1, ZHU Chenghong1, LIU Jiahui2, HU Tianyue2,†   

  1. 1. Petroleum Exploration and Production Research Institute, Sinopec, Beijing 100083 2. School of Earth and Space Sciences, Peking University, Beijing 100871
  • Received:2020-12-02 Revised:2021-01-26 Online:2021-11-20 Published:2021-11-20
  • Contact: HU Tianyue, E-mail: tianyue(at)pku.edu.cn

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摘要：

针对陆地层间多次波压制效果不明显的问题, 提出降维虚同相轴法, 实现叠前陆地地震资料的层间多次波压制。对经过精确动校正的叠前道集, 逐道应用虚同相轴法预测层间多次波, 实现降维处理。与传统的虚同相轴法相比, 运算量大大降低, 且不再要求炮检点规则且密集地分布。同时, 引入加权叠加的高信噪比参考道, 参与叠前道集的互相关和褶积运算, 提高了虚同相轴法的预测精度。将所提方法应用于中国西部地区实际陆地地震资料, 取得明显的层间多次波压制效果。

关键词: 层间多次波, 虚同相轴, 降维, 互相关, 自适应相减

Abstract:

As the land internal multiples could not been obviously attenuated, the authors develop the dimensionality reduced virtual event method to suppress the internal multiples in pre-stack land seismic data. Compared with the traditional virtual event method, the authors apply the virtual event method trace by trace on the pre-stack gathers after accurate dynamic correction to predict internal multiples. It achieves dimensionality reduction, greatly reduces the amount of calculation, and no longer requires a regular and dense enough distribution. Meanwhile, this method introduces a weighted reference trace with high signal-to-noise ratio to participate in the cross-correlation and convolution operations of the pre-stack gathers, which improves the prediction accuracy of the virtual event method. This method is applied to actual land seismic data in western China and achieved obvious effect when suppressing internal multiples.

Key words: internal multiples, virtual event, reduced dimensionality, correlation, adaptive subtraction

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