High-precision seismic data reconstruction based on three-dimensional curvelet transform

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.