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基于二阶系统的NCPML吸收边界三维声波逆时偏移方法 被引量:3

Three-dimensional acoustic reverse time migration with a NCPML absorbing boundary condition in a second-order system
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摘要 二阶声波方程逆时偏移中,常规分裂完全匹配层(SPML)吸收边界条件是目前比较常用的吸收边界条件,但是常规SPML吸收边界条件存在变量个数多、计算存储量大、计算效率低下等缺点,影响了三维逆时偏移的实际应用。为此引进一种新的卷积完全匹配层(NCPML)吸收边界条件,将其拓展应用于三维声波方程正演模拟,然后应用于三维逆时偏移。该方法基于SPML吸收边界条件,忽略复数频率域中衰减因子的空变特性,反变换至时间域即得到二阶系统下声波方程的NCPML吸收边界条件。均匀介质模型实验表明,NCPML吸收边界条件在数值模拟中计算效率和内存占用上较常规SPML吸收边界条件更优。SEG/EAGE推覆体模型和实际资料的三维逆时偏移实验结果表明,NCPML吸收边界条件具有更好的稳定性。 In reserve time migration(RTM)based on a second-order system,a split perfectly matched layer(SPML)absorbing boundary condition is currently widely used.However,SPML is not applicable to three-dimensional RTM because of the large number of variables,large memory requirement,and low efficiency.A convolutional perfectly matched layer(NCPML)absorbing boundary condition was therefore applied to the three-dimensional forward modeling and RTM of the acoustic equation.In the proposed method,the SPML was first applied,ignoring the spatial variation of the damping coefficient in the complex-frequency domain.Then,the inverse Fourier transform was performed to revert to the time domain and obtain the NCPML absorbing boundary condition for the second-order acoustic wave equation.Numerical tests demonstrated the superiority of the NCPML over the SPML in terms of memory economy,efficiency,and robustness.
作者 李青阳 吴国忱 杨凌云 王玉梅 LI Qingyang;WU Guochen;YANG Lingyun;WANG Yumei(School of Geosciences,China University of Petroleum (East China),Qingdao 266580,China;Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266071,China;Geophysical Resarch Institute of Shengli Oilfield,Sinopec,Dongying 257022,China)
出处 《石油物探》 EI CSCD 北大核心 2020年第6期901-911,935,共12页 Geophysical Prospecting For Petroleum
基金 国家科技重大专项(2016ZX05024-001-008)资助。
关键词 卷积完全匹配层 二阶系统 声波方程 逆时偏移 内存优化 计算效率 鲁棒性 convolutional perfectly matched layer second-order system acoustic wave equation reserve time migration memory optimization computational efficiency robustness
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