基于二阶系统的NCPML吸收边界三维声波逆时偏移方法

2020年 59卷 第No. 6期
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Three-dimensional acoustic reverse time migration with a NCPML absorbing boundary condition in a second-order system
(1.中国石油大学(华东)地球科学与技术学院,山东青岛266580;2.海洋国家实验室海洋矿产资源评价与探测技术功能实验室,山东青岛266071;3中国石油化工股份有限公司胜利油田分公司物探研究院,山东东营257022)
(1.School of Geosciences,China University of Petroleum (East China),Qingdao 266580,China;2.Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266071,China;3.Geophysical Resarch Institute of Shengli Oilfield,Sinopec,Dongying 257022,China)

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.

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;


基金项目:国家科技重大专项(2016ZX05024-001-008)资助。

10.3969/j.issn.1000-1441.2020.06.008