Well log interpretation involves analyzing geological structures and reservoir contents using well log data. The advancement of artificial intelligence has led to the wide-ranging application of deep learning in well log interpretation. However, due to cost limitations, only a small number of wells are typically surveyed and tested in each logging block, posing a significant challenge in achieving accurate interpretation under few-shot conditions. To tackle this challenge, we propose a data augmentation method that integrates time-domain and frequency-domain features of well log curves. This method aims to harness the characteristics of well log curves in both domains to enrich the limited data. Furthermore, we have developed a neural network architecture search space tailored to the few-shot well log data problem and demonstrated the effectiveness of the proposed time-domain and frequency-domain data augmentation for well log interpretation. Our method has shown the ability to achieve accuracies of up to 93.00 \(\%\) and 95.05 \(\%\) on well logging interpretation tasks when applying different augmentation methods to the data from different blocks. These performance indicators are comparable to the results of training with all training wells under the entire block.