Application of data augmentation based on generative adversarial network in impedance inversion
In recent years, various deep learning techniques have been widely used in the field of geophysics. As far as seismic impedance inversion is concerned, a nonlinear mapping model from seismic data to wave impedance can be established by training the depth inversion network, and then the impedance information can be predicted by the nonlinear model. However, the effectiveness of the current impedance inversion methods based on deep neural networks depends on the number of labels. The generalization ability of the model trained in the state of few labels is poor. Data augmentation can alleviate this situation by using the existing data. Therefore, the author proposes a method based on generative adversarial network (GAN) to augment the labels in the original data set, and uses geophysical forward modeling technology to forward seismic data to achieve the function of data augmentation. Unlike existing GAN, which generates samples directly from noise, the method augments the labeled data from the original dataset. The validity of this method is verified by model data and actual data. The method provides a data augmentation seismic inversion technique based on GAN for impedance inversion.
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