A data augmentation method for semi-airborne transient electromagnetic noise based on a generative adversarial network
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.
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