High-Resolution Imaging: An Approach by Compensating Absorption and Dispersion in Prestack Time Migration With Effective Q Estimation and Fresnel Zone Identification Based on Deep Learning - Wu, Jizhong; Shi, Ying; Guo, Aihua; Lu, Pengfei; Yang, Qianqian (Frontiers in Earth Science, 2022)
We have developed a migration scheme that can compensate absorption and dispersion with effective Q estimation and Fresnel zones identification based on deep learning. We use U-Net neural network technology in deep learning to automatically identify Fresnel zones from compensated migrated dip-angle gathers and obtain the optimal aperture for migration, avoiding the tedious task of manually modifying the boundaries of Fresnel zones. Instead of interval Q factor, we used an effective Q parameter to compensate absorption and dispersion. The effective Q is estimated using VSP well data and surface seismic velocity data. The proposed scheme can be incorporated into conventional seismic data processing workflow. A field data set was employed to validate the proposed scheme. Higher-resolution imaging results with low noise levels are obtained.