Semi-Supervised Deep Learning Seismic Impedance Inversion Using Generative Adversarial Networks

Deep learning methods have been successfully applied to solve seismic inversion problems in recent years. Though deep learning inversion can obtain results with much higher resolution compared to geophysical inversion, its performance often suffers from the limitation of the well logs which are main source of labels in training data. To overcome this problem, we propose a semi-supervised deep learning workflow based on Generative Adversarial Network (GAN) for seismic impedance inversion. The workflow contains three networks: a generator, a discriminator, and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. Test on Marmousi2 model shows that, by making use of both labeled and unlabeled data, the proposed method predicts impedance with better consistency than conventional deep learning inversion.