Seismic Impedance Inversion Using Conditional Generative Adversarial Network
Delin Meng, Bangyu Wu, Zhiguo Wang, Zhaolin Zhu
Abstract
Deep-learning methods, such as convolutional neural networks (CNNs), have been successfully applied to seismic impedance inversion in recent years. Compared with traditional geophysical inversion, deep-learning inversion can give inversion results with higher resolution. In this letter, we further improve the performance of deep-learning inversion and propose a seismic impedance inversion method based on conditional generative adversarial network (cGAN). In the proposed method, a generator learns to predict seismic impedance from seismic data, and a discriminator learns to distinguish between fake and real impedance. We mix the cGAN objective with mean square error (MSE) loss to bring in more information for model training. Besides, a CNN-based seismic forward model is trained to introduce the constraint of unlabeled data in the training of cGAN. Tests on Marmousi2 model and overthrust model show that the proposed method can obtain more accurate impedance and have better robustness against random noise than CNN method.