Refitting the Past v4: Structural Translation Forecasting – Final Edition

This work introduces and finalizes the Structural Translation Forecasting framework—a novel methodology for reinterpreting historical seismic data within modern environmental and structural contexts. Using parameterized stress refitting equations and region-specific coefficients (e.g., α = 0.1 ± 0.02 for urban density, β = 0.05 ± 0.01 for hydrological pressure), we simulate structural changes under 2025 conditions. The approach is validated with both a Kobe 1995 case study and a Tohoku 2011 refit simulation. AI-based predictions utilizing Graph Neural Networks (GNNs) outperform conventional LSTM models, showing an RMSE improvement from 0.21 to 0.15. Appendices include regional parameter variance, seasonal effects, and extended sensitivity analysis. This work establishes structural translation forecasting as a scalable, interpretable, and real-world-relevant methodology for seismic risk modeling.