2026-10-11T16:03:01Z https://www.scidb.cn/oai

10.57760/sciencedb.20541 2025-02-22T21:49:30Z

2025-02-22 High-precision copper-grade identification via a vision transformer with PGNAA doi:10.57760/sciencedb.20541 en To construct the dataset required for machine learning, the gamma spectrum of copper ore was obtained using MCNP. The material card of the MCNP was set according to the actual copper mineral composition. The primary elementsin the Cu ore and their contents are listed in Table 2. In total, 4400 energy spectrum data points were obtained. Based on the copper content, the data were divided into five categories: gangue (0 to 0.2%), industrial-grade copper ore (0.2 to 0.5%), low-grade copper ore (0.5 to 1.5%), medium-grade copper ore (1.5 to 2%), and high-grade copper ore (2 to 3%). Cu is associated with minerals such as pyrite (FeS 2 ), sphalerite(ZnS),galena(PbS),andcobaltite(CoAsS)[39]. Therefore, copper ores often contain associated minerals, such as Pb, Zn, Fe, and Co. Machine-learning models were used to identify the presence of associated minerals in the copper ore. Ores containing Pb, Zn, Fe, and Co below the cut-off grade were designated as gangue, whereas those above the cut-off grade were designated as minerals, based on the content of the associated elements in the ore。 Copper grade identification; prompt gamma neutron activation analysis; Monte Carlo N-Particle; vision transformer ChongGui Zhong PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1007/s41365-025-01684-6 Science Data Bank