Seismic impedance inversion based on cycle-consistent generative adversarial network

Abstract Deep learning has achieved great success in a variety of research fields and industrial applications. However, when applied to seismic inversion, the shortage of labeled data severely influences the performance of deep learning-based methods. In order to tackle this problem, we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network (Cycle-GAN). The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets. Three kinds of loss, including cycle-consistent loss, adversarial loss, and estimation loss, are adopted to guide the training process. Benefit from the proposed structure, the information contained in unlabeled data can be extracted, and adversarial learning further guarantees that the prediction results share similar distributions with the real data. Moreover, a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model. The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases. And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.