【摘要】 随着地震勘探正在蓬勃发展,如今对储层预测精度和地震资料质量的要求越来越高。而地震勘探的复杂环境可能导致地震数据通道缺失或者勘探成本上升,因此需要对地震数据进行重建,恢复地震数据的全貌。针对上述情况,首先介绍Bregman迭代方法,接着在Bregman迭代重建算法框架中,使用K-SVD对数据样本进行初步的处理,每次迭代最后进行插值处理,进行多次迭代后得出重建的地震数据。主要将K-SVD字典训练算法结合到分裂Bregman迭代过程之中,实现对缺失地震数据进行重建研究,以保证地震数据具有完整性和规则性,从而提高地震数据的信噪比和保真度。采用marmousi数据,验证了本文算法的可行性与有效性。更多还原

【Abstract】 With the rapid development of seismic exploration,the demand for reservoir prediction accuracy and seismic data quality is becoming higher and higher.The complex environment of seismic exploration may lead to the lack of seismic data trace or the increase of exploration cost,so it is necessary to reconstruct the seismic data and restore the complete picture of seismic data.In view of the above situation,we first understand the method of Bregman iteration,then in the framework of Bregman iterative reconstruction algorithm,we use K-SVD to process the data sample,and interpolate the data at the end of each iteration.After many iterations,the reconstructed seismic data are obtained.The K-SVD dictionary training algorithm is mainly combined with the split Bregman iterative process to reconstruct the missing seismic data,so as to ensure the integrity and regularity of the seismic data.An experiment uses marmousi data to verify the feasibility and effectiveness of the proposed algorithm.更多还原