High-precision copper-grade identification via a vision transformer with PGNAA
摘要
The identification of ore grades is a critical step in mineral resource exploration and mining. Prompt gamma neutron activation analysis (PGNAA) technology employs gamma rays generated by the nuclear reactions between neutrons and samples to achieve the qualitative and quantitative detection of sample components. In this study, we present a novel method for identifying copper grade by combining the vision transformer (ViT) model with the PGNAA technique. First, a Monte Carlo simulation is employed to determine the optimal sizes of the neutron moderator, thermal neutron absorption material, and dimensions of the device. Subsequently, based on the parameters obtained through optimization, a PGNAA copper ore measurement model is established. The gamma spectrum of the copper ore is analyzed using the ViT model. The ViT model is optimized for hyperparameters using a grid search. To ensure the reliability of the identification results, the test results are obtained through five repeated tenfold cross-validations. Long short-term memory and convolutional neural network models are compared with the ViT method. These results indicate that the ViT method is efficient in identifying copper ore grades with average accuracy, precision, recall,