Transformer-based deep learning architecture for multivariable radioactive source term inversion

Inversion for the radioactive source term has received growing attention in the post-Fukushima era. Under some special scenarios, the source term, including release rate, height and position, is necessary for nuclear emergency response and consequence assessment. Here, a transformer-based deep learning architecture was developed for multivariable source term estimation. The CALMET-LAPMOD coupling model validated by the Kincaid tracer experiment was employed to produce the datasets. The datasets were systematically constructed for five representative scenarios with the following time-varying parameters: release rate, release height, release location, coupling of release rate and height, and coupling of all three variables. Subsequently, a Transformer model with Bayesian optimization for adaptive hyperparameter tuning was developed. The results demonstrated excellent performance in source term inversion, with a determination coefficient (R²) of above 0.96 for release rate and height, and an average distance error of 1.19 km at a 95 % confidence level for location prediction. Regarding the coupling of all three variables scenario, the R² for release rate and location remained above 0.92, whereas the height achieved R² of 0.72. Additionally, feature ablation analysis revealed that monitoring points with high concentration values contribute significantly to inversion, providing quantitative insights to optimize the monitoring network layout.