A Data‐Driven High‐Resolution Imaging Based on the Cost‐Effective Construction of Point‐Spread Functions

ABSTRACT Least‐squares migration (LSM) is one of the most accurate imaging methods in seismic exploration. In recent years, image‐domain LSM (ID‐LSM) based on the approximated Hessian matrix has received widespread attention and development. How to effectively represent the Hessian matrix and implement the ID‐LSM efficiently and stably remains challenging. This study proposes an efficient computation method for the Hessian matrix and develops a data‐driven high‐resolution imaging scheme to promote the application of LSM. Specifically, we first introduce the analytical expression of the Hessian matrix within the framework of inversion imaging, leveraging the sparsity of the Hessian matrix and using point‐spread functions (PSFs) to approximate it. Then, considering the nonlinear characteristics of image‐domain PSFs deconvolution, we employ deep learning to construct a data‐driven imaging correction network. Finally, we incorporate features from the target data into the network, achieving efficient and faithful imaging of subsurface reflection coefficients. Through the computation cost analysis of PSFs construction, the developed method significantly reduces the computational costs, achieving only one‐fourteenth of the modelling–migration method based on the wave equation. The synthetic and field data examples demonstrate the effectiveness of the proposed data‐driven imaging scheme in both the spatial and wavenumber domains.