Research on Radionuclide Identification Method Based on GASF and Deep Residual Network

In nuclide identification, traditional methods based on nuclide library comparisons rely on the identification of characteristic peaks, often overlooking the full spectrum information, which leads to cumbersome operations and low efficiency. In recent years, machine learning and deep learning techniques have been introduced into the field of nuclide recognition to improve identification efficiency; however, most existing methods fail to effectively extract deep features from the data. To address this issue, this paper proposes a method that integrates the Gram Angular Summation Field (GASF) algorithm with a Deep Residual Network (ResNet) for processing nuclide energy spectrum data. First, the GASF algorithm is used to transform one-dimensional spectral data into two-dimensional images, thereby fully extracting spatial features from the data. Then, these two-dimensional images are input into the ResNet model, where features are automatically extracted through multiple convolutional layers. Finally, the Softmax layer is used for nuclide classification. Experimental results demonstrate that the proposed method can effectively improve both the accuracy and efficiency of nuclide identification; the recognition accuracy on the simulated data reaches 99.5%, and, when tested with actual measurement data containing unknown radionuclides, the model still achieves a high accuracy of 92.6%. This study shows that the combination of deep learning and signal processing techniques can significantly improve the accuracy and application scope of nuclide identification, offering substantial practical value.