Image-Domain Least-Squares Migration via a Deep Feature Deconvolution Network

Abstract

Least-squares migration (LSM) improves seismic imaging resolution by compensating for Hessian-induced blurring, but the explicit construction and inversion of the full Hessian remain computationally prohibitive. Image-domain LSM (ID-LSM) commonly uses point-spread functions (PSFs) as local representations of the Hessian. However, conventional PSF deconvolution is sensitive to spatial nonstationarity and ill-posedness, and often suffers from a tradeoff between resolution enhancement and artifact or noise amplification. To alleviate this problem, we propose an ID-LSM method based on a deep feature deconvolution network (DFDN). The migrated image is first mapped into a deep feature space, where explicit PSF deconvolution is performed in the feature domain, and the compensated features are then fused through a reconstruction network to recover the final image. In addition, a physics-consistency constraint derived from the PSF degradation model is introduced to improve both stability and physical plausibility. Numerical experiments demonstrate that, compared with reverse time migration (RTM) and standard ID-LSM, the proposed method produces higher-quality imaging results.