Semi-supervised Seismic Wave Impedance Inversion Based on Generative Adversarial Networks
Deep learning impedance inversion usually requires a large amount of labeled data for network train-ing.However,in practical applications,labeled data(well logging data)is often limited.To address this issue,this paper proposes a new semi-supervised seismic inversion method based on Generative Adversarial Net-works(GANs).The method improves the traditional GAN network structure by using a conditional GAN(cGAN),redesigning the generator with a Unet structure and the discriminator with a Resnet structure,and employing Wasserstein GAN(WGAN)to construct a new objective function.The network training is divided in-to two stages:first,training the discriminator with a small amount of labeled data,then,training the generator with a small amount of labeled data and a large amount of unlabeled data,where the generator is constrained by the forward convolutional model.Experimental results on synthetic data demonstrate that the proposed method is suitable for impedance inversion with limited labeled data,accurately reconstructs impedance models,and ex-hibits good noise resistance.Inversion results on real field data also indicate the practicality of this method.This method offers a novel approach for addressing the challenge of limited labeled data in seismic wave impedance inversion,holding promising prospects for practical applications.
deep learningseismic wave impedance inversiongenerative adversarial networksemi-super-vised learning
万方数据
维普
