Abstract We propose a controllable source electromagnetic inversion method based on convolution neural network(CNN) and generated countermeasure network(GAN). In order to highlight the information of the abnormal body, we preprocess the total field by difference, and modify the loss function to enhance the stability of the GAN. By sending the difference total field into CNN, we obtain the structural causal relationship between the ground receiving electric field and the conductivity data of the underground abnormal body; the rough conductivity model of the abnormal body is output. Then as the input of GAN, the features are extracted in GAN for training, and we obtain the conductivity inversion results with high precision and high contrast, which meets the requirements of engineering application. In comparison, the CNN-GAN combined model is better than the traditional neural network model, and can better predict the electrical conductivity model of underground abnormal bodies; the similarity is as high as 94.38%, which is about 48% and 78% higher than the traditional CNN and GAN model, respectively.
MA Yanqi,LI Weiqin,WU Yuhan et al. A Controllable Source Electromagnetic Inversion Method Based on CNN-GAN Combinatorial Model[J]. jgg, 2023, 43(10): 1095-1100.
MA Yanqi,LI Weiqin,WU Yuhan et al. A Controllable Source Electromagnetic Inversion Method Based on CNN-GAN Combinatorial Model[J]. jgg, 2023, 43(10): 1095-1100.