Impact of reactive mineral facies distributions on radionuclide sorption properties in multiscale heterogeneous granite rocks

Modeling radionuclide reactive transport in host rocks is one of the major challenges with respect to the performance and safety assessment of high-level radioactive repositories. The rock heterogeneity has a significant impact on the scale effects of flow and solute-transport parameters such as the sorption coefficient. The scaling of the radionuclide sorption coefficient is related to the reactive mineral facies (RMF) distributions in fractured rocks, which can be simulated with transition probability-based geostatistical methods. Geostatistical and geochemical analyses of the RMF distributions are performed on granite samples taken from the Beishan site in northwest China. This paper presents an approach to identifying RMF by using a deep-learning-based mineral identification model. The volume proportions and mean lengths of the RMF are accurately estimated in this way compared to the results of X-ray diffraction analysis. This approach overcomes the limitations of traditional methods which are subjective and have lower accuracy. The results show that the composite covariance model of sorption coefficients is related to the volume proportion and mean length of the RMF. The effective sorption coefficient obtained by the upscaling model is greater than the geometric average and smaller than the arithmetic average. Furthermore, through global sensitivity analysis, it is found that mean retardation factors have the most significant effect on the effective sorption coefficient. The results of this study provide substantial information that contributes to improving understanding of radionuclide transport in fractured granite.