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

Abstract

We have developed a migration scheme that can compensate absorption and dispersion with effective Q estimation and Fresnel zone identification based on deep learning. We use the 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 the 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.