Stratigraphically Constrained Well-Log Data Augmentation for Angle-Stack AVO Forward Modelling

Deep learning based seismic elastic inversion workflows strongly benefit from realistic synthetic angle-stack seismic data, especially where field data are limited or unavailable. This study presents a stratigraphically constrained workflow for exact Zoeppritz-based amplitude variation with offset (AVO) forward modelling using augmented well-log data. The methodology integrates P-wave velocity (Vp), S-wave velocity (Vs), and density (ρ) logs from real wells with interpreted seismic horizons to generate geologically consistent synthetic elastic models and angle-dependent seismic responses. Within each stratigraphic interval defined by seismic horizons, multivariate statistics of Vp, Vs, and ρ are estimated across all available wells, preserving intrinsic elastic parameter correlations. Synthetic wells are then generated through multivariate data augmentation conditioned to these statistics and constrained by the stratigraphic framework. The resulting well-log data are used for AVO forward modelling based on the exact Zoeppritz equations, computing angle-dependent P-wave reflection coefficients at elastic interfaces. These reflection coefficients are subsequently convolved with a seismic wavelet to generate synthetic angle stacks. The proposed workflow produces consistent sets of synthetic elastic wells logs and exact Zoeppritz-based angle-stack data, providing realistic and physically grounded training datasets for seismic elastic inversion.