论文详情
随着国内油气勘探的不断深入，地震勘探目标越来越精细，常规的地震数据规则采集方式投资成本高昂，而非规则采集方式能够在不增加投资的情况下，得到非规则三维数据体。对非规则数据进行高密度的规则化重建，成为当前处理成像要解决的关键技术问题。针对该问题，基于压缩感知理论，研究了三维曲波变换数据重建方法。利用曲波变换能够有效捕捉地震记录中同相轴的各向异性特征以及方向性和各向异性特点，对地震数据同相轴进行最优稀疏表达，再引入凸集投影算法（POCS），开展基于三维曲波变换的非规则地震数据重建，提高重建精度。同时，采用f-x域转换和OpenMP并行加速优化策略提高方法的计算效率，最终实现了基于压缩感知的非规则采集数据高密度、高效率、高精度重建。利用该数据重建技术对胜利油田广利—青南滩浅海三维非规则采集数据进行重建和成像处理，结果表明：该方法重建结果精度高、计算效率高，能够获得较常规规则高密度采集更好的偏移剖面，明显提高了勘探目标的分辨能力。
In China, seismic surveys deal with increasingly small and complex targets. Improved seismic resolution requires the downsizing of underground sampling grids. Conventional regular sampling methods are extremely costly. Compressed-sensing irregular sampling can design non-equally spaced shot and receiver points, without increasing investment, to obtain a uniform discrete distribution of CMP points and an irregular 3D data volume. The regularized reconstruction of irregular data with higher density has become a key issue in imaging. There are various reconstruction methods, most of which cannot balance accuracy and efficiency. Based on the compressed sensing theory, this paper uses a reconstruction method based on 3D curvelet transform, which can effectively capture the anisotropic and orientation features of seismic events for their optimal sparse representation. An algorithm of projection onto convex sets (POCS) is introduced to improve reconstruction accuracy. An optimization strategy with f-x domain conversion and OpenMP parallel acceleration is used to improve computational efficiency. This method realizes the reconstruction of irregularly acquired data with high density, high efficiency, and high precision based on compressed sensing. The application to the Guangli-Qingnantan shallow sea survey in Shengli Oilfield shows that the proposed method has high accuracy, high computational efficiency, and better imaging with improved resolution than a conventional regularly sampled high-density survey.