论文详情
With the development of oil and gas exploration,the scale and complexity of collected data are increasing.The reconstruction of seismic missing data is essential for subsequent data processing.Reconstruction algorithms based on compressive sensing are accurate and efficient.Here,we proposed a seismic data reconstruction method,based on a spectral projection gradient L1 algorithm (SPGL1) and on compressive sensing,which can be applied to large-scale and complex seismic data.First,a sampling matrix was selected according to the missing data.Then,the missing sparse coefficients were reconstructed using the SPGL1 in the contourlet domain.Finally,the contourlet inverse transform was used to reconstruct the seismic data.Tests on synthetic and field data demonstrated the superiority of the proposed method over traditional methods:it provided higher accuracy and efficiency.Based on the contourlet transform,we could conclude that the SPGL1 is more robust than OMP and the gradient projection algorithm GPSR in the processing of noisy data.
国家重点研发计划课题“天然气水合物高精度三维地震数据处理和成像技术研究”(2017YFC0307405)资助。