Huber inversion-based reverse-time migration with de-primary imaging condition and curvelet-domain sparse constraint  

作　　者：Bo Wu Gang Yao Jing-Jie Cao Di Wu Xiang Li Neng-Chao Liu 

机构地区：[1]State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum(Beijing),Beijing,102249,China [2]Unconventional Petroleum Research Institute,China University of Petroleum(Beijing),Beijing,102249,China [3]Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration,Ministry of Natural Resources,Hebei GEO University,050031,China [4]Hebei Key Laboratory of Strategic Critical Mineral Resources,Hebei GEO University,Shijiazhuang,Hebei,050031,China [5]College of Geophysics,China University of Petroleum(Beijing),Beijing,102249,China

出　　处：《Petroleum Science》2022年第4期1542-1554,共13页石油科学（英文版）

基　　金：supported by National Key R&D Program of China (No. 2018YFA0702502);NSFC (Grant No. 41974142, 42074129, and 41674114);Science Foundation of China University of Petroleum (Beijing) (Grant No. 2462020YXZZ005);State Key Laboratory of Petroleum Resources and Prospecting (Grant No. PRP/indep-42012)。

摘　　要：Least-squares reverse-time migration(LSRTM) formulates reverse-time migration(RTM) in the leastsquares inversion framework to obtain the optimal reflectivity image. It can generate images with more accurate amplitudes, higher resolution, and fewer artifacts than RTM. However, three problems still exist:(1) inversion can be dominated by strong events in the residual;(2) low-wavenumber artifacts in the gradient affect convergence speed and imaging results;(3) high-wavenumber noise is also amplified as iteration increases. To solve these three problems, we have improved LSRTM: firstly, we use Hubernorm as the objective function to emphasize the weak reflectors during the inversion;secondly, we adapt the de-primary imaging condition to remove the low-wavenumber artifacts above strong reflectors as well as the false high-wavenumber reflectors in the gradient;thirdly, we apply the L1-norm sparse constraint in the curvelet-domain as the regularization term to suppress the high-wavenumber migration noise. As the new inversion objective function contains the non-smooth L1-norm, we use a modified iterative soft thresholding(IST) method to update along the Polak-Ribie re conjugate-gradient direction by using a preconditioned non-linear conjugate-gradient(PNCG) method. The numerical examples,especially the Sigsbee2 A model, demonstrate that the Huber inversion-based RTM can generate highquality images by mitigating migration artifacts and improving the contribution of weak reflection events.

关 键 词：Least-squares reverse-time migration Huber-norm Sparse constraint Curvelet transform Iterative soft thresholding 

分 类 号：P618.13[天文地球—矿床学]