基于快速字典学习的压缩感知地震数据重建  

Compressed sensing seismic data reconstruction based on fast dictionary learning

作　　者：李婷婷 段中钰[1] LI Tingting;DUAN Zhongyu(College of Information and Communication Engineering,Beijing Information Science and Technology University,Beijing 100101,China)

机构地区：[1]北京信息科技大学信息与通信工程学院,北京100101

出　　处：《物探化探计算技术》2022年第1期9-16,共8页Computing Techniques For Geophysical and Geochemical Exploration

基　　金：北京市教委科研计划项目(KM201811232009)。

摘　　要：地震数据的稀疏性是压缩感知地震数据重建的重要前提,其直接影响地震数据的重建精度,因此研究高效的地震数据稀疏表示方法具有重要意义。针对经典K-SVD算法稀疏编码时无法得到全局最优解,不能保证收敛从而影响重建精度的问题,这里提出快速字典学习算法稀疏表示地震数据的方法。快速字典学习将稀疏表示目标优化问题转换为两个可直接求解最值的子优化问题,且将稀疏约束上限与字典相干性关,将快速字典学习算法应用于压缩感知地震数据重建。模拟地震数据以及大庆油田实际地震数据仿真验证结果表明,基于快速字典学习的压缩感知地震数据重建不仅能更好地重建地震数据细节,而且耗时少。The sparsity of seismic data is an important prerequisite for compressed sensing seismic data reconstruction,which directly affects the accuracy of seismic data reconstruction.Therefore,it is of great significance to study efficient seismic data sparse representation methods.Aiming at the problem that the classical K-SVD algorithm cannot obtain the global optimal solution when sparse coding,and cannot guarantee convergence,which affects the reconstruction accuracy,we propose a fast dictionary learning algorithm to sparsely represent seismic data in this article.Fast dictionary learning transforms the sparse representation target optimization problem into two sub-optimization problems that can directly solve the most value,and the sparse constraint upper limit is related to the dictionary coherence,and the fast dictionary learning algorithm is applied to compressive sensing seismic data reconstruction.Simulation verification results of simulated seismic data and actual seismic data of Daqing Oilfield show that compressed sensing seismic data reconstruction based on fast dictionary learning can not only better reconstruct seismic data details,but also takes less time.

关 键 词：压缩感知 字典学习 稀疏表示 地震数据重建 

分 类 号：P631.4[天文地球—地质矿产勘探]