Abstract: The semi-airborne transient electromagnetic (SATEM) noise data, exhibiting intricate forms, high acquisition costs, and small volumes, cannot be augmented using conventional augmentation methods, thus significantly hindering the subsequent denoising work. Hence, this study proposed a data augmentation method for SATEM signals based on the generative adversarial network (GAN). By designing the generator as a long short-term memory (LSTM) network and training the generator and discriminator models based on the dataset of real-measured SATEM noise, this study obtained a generator model that can generate simulated noise data. Then, this study analyzed the distributions of the simulated noise generated by the generator and the real-measured noise. Moreover, this study compared the performance of the denoising network before and after augmentation, demonstrating the effectiveness of this method for augmenting real-measured SATEM noise data.