Zhang Xiaodan, Li Rui, Cui Lin, Liu Dongxiao, Liu Guizhong, Zhang Zhiyu
School of Electronic Information, Xi'an Polytechnic University, Xi'an, 710060, China.
School of Information and Communications Engineering, Xi'an JiaoTong University, Xi'an, China.
Sci Rep. 2023 Aug 21;13(1):13623. doi: 10.1038/s41598-023-40578-8.
Least squares reverse time migration (LSRTM) imaging is the one of the most accurate methods for migration imaging at present, and Polak-Ribiere-Polyak conjugate gradient (PRPCG) for LSRTM has the good numerical performance but weak convergence, so we construct an optimization factor to improve the iteration direction of the gradient, which can automatically generate a sufficient descent direction. The improved PRPCG (IPRPCG) can reduce the data residual values and speed up the iteration. And the illumination preconditioned (IP) operator is employed to IPRPCG-LSRTM which solves the problem of low resolution due to the insufficient iterative gradient information. In this paper, the experiments show that the imaging results of the proposed method (IPRPCG-IP-LSRTM) is improved greatly in detail characterization and events continuity, the iterative curve converged faster significantly, and the normalized data residual was reduced by 6.55% on average, which improved the accuracy of migration imaging effectively.
最小二乘逆时偏移（LSRTM）成像是目前偏移成像最精确的方法之一，用于LSRTM的Polak-Ribiere-Polyak共轭梯度（PRPCG）具有良好的数值性能但收敛性较弱，因此我们构造一个优化因子来改进梯度的迭代方向，该因子能自动生成充分的下降方向。改进后的PRPCG（IPRPCG）能够降低数据残差值并加速迭代。并且将照明预处理（IP）算子应用于IPRPCG-LSRTM，解决了由于迭代梯度信息不足导致的分辨率低的问题。本文实验表明，所提方法（IPRPCG-IP-LSRTM）的成像结果在细节刻画和同相轴连续性方面有很大改善，迭代曲线收敛明显加快，归一化数据残差平均降低了6.55%，有效提高了偏移成像的精度。
