1. College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao　 266590,China; 2. Shandong Province Key Labo-ratory of Wisdom Mine Information Technology,Shandong University of Science and Technology,Qingdao　 266590,China

由于采集环境及仪器性能的限制,采集到的地震勘探数据经常是不规则和不完整的,进而影响到地震数据后续处理及反演,因此在对地震数据进行下一步分析处理前有必要先重建出完整的地震数据,提出了一种基于改进的Curvelet域的地震数据压缩重建算法。首先在压缩感知理论的框架下,利用Curvelet的稀疏特性,建立缺失地震数据重建模型;然后在CRSI(Curvelet Recovery by Sparsity-Promoting Inversion,CRSI)算法框架基础上,采用改进的指数阈值方法,对缺失地震数据进行恢复重建。使用了4层水平均匀介质模型和Marmousi模型模拟的地震数据进行了随机稀疏采样和重建的数值实验。实验结果表明,与传统重建算法比较,该方法不仅加快了原有算法的收敛速度,同时保证了重建数据的高信噪比,验证了所提方法的可行性和有效性。

As a result of the acquisition of environment and instrument performance constraints,seismic data collected are often irregular and incomplete. Thus it is necessary to reconstruct the complete seismic data before proceeding to the next step in the seismic data analysis. The authors present a modified Curvelet algorithm for image reconstruction based on seismic compression. First in the framework of compressed sensing theory,using the sparse characteristic of Curvelet,a missing data reconstruction model is built,and then using the CRSI(Curvelet Recovery by sparsity-promo- ting Inversion,CRSI) algorithm framework,adopting improved exponential threshold algorithm,the missing seismic da- ta are restored and reconstructed. In this paper,the authors use four level homogeneous medium model and the seismic data simulated by Marmousi model to carry out numerical experiments of random sparse sampling and reconstruction. The result of the experiment shows that compared with the traditional recon-struction algorithm,the proposed method not only accelerates the convergence speed of the original algorithm,but also guarantees a high SNR of the reconstruc- ted data,which verifies the feasibility and effectiveness of the proposed method.

国家重点研发计划资助项目(2016YFC0801406)；山东省自然科学基金资助项目(ZR2018MEE008)；山东省重点研发计划资助项目(2016GSF120012)；

HOU Wenlong,JIA Ruisheng,SUN Yuanyuan,et al. Seismic data reconstruction method based on improved curvelet transform[J]. Journal of China Coal Society,2018,43(9):2570-2578.