A data augmentation method for semi-airborne transient electromagnetic noise based on a generative adversarial network
FENG Wei1(), FENG Hao2, XIAO Li-Jiang1, CHEN Pin-Ming1, LIU Dong2,3, WANG Yong-Xin2,4(), ZHOU Xiao-Sheng2,3, SUN Huai-Feng2, WANG Zhen2,5
1. Zhejiang Communications Construction Group Co., Ltd., Hangzhou 310000, China 2. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, China 3. Guangxi Communications Investment Group Corporation Ltd., Nanning 530022, China 4. Shandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, China 5. Beijing Research Institute of Shandong University, Beijing 100873, China
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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FENG Wei, FENG Hao, XIAO Li-Jiang, CHEN Pin-Ming, LIU Dong, WANG Yong-Xin, ZHOU Xiao-Sheng, SUN Huai-Feng, WANG Zhen. A data augmentation method for semi-airborne transient electromagnetic noise based on a generative adversarial network. Geophysical and Geochemical Exploration, 2024, 48(3): 812-819.
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