Pre-stack seismic inversion for elastic parameters using model-data-driven generative adversarial networks

Model-data-driven (MDD) generative adversarial networks (GANs) employing pre-stacked seismic data to estimate elastic parameters are proposed. First, by traverse sampling of elastic parameter model space and Gaussian sampling, a complete elastic parameter dataset is generated. Second, a number of reflection coefficient sequences are constructed to produce synthetic pre-stack amplitude-versus-angle (AVA) gathers using the Zoeppritz equations. Third, conditional GANs (CGANs) are trained using synthetic datasets to establish a relationship between the synthetic pre-stack AVA gathers and elastic parameters. By employing low-frequency constraints, the absolute values of the elastic parameters from the pre-stack AVA gathers are computed. To apply the networks trained with synthetic to field seismic data, Gaussian noise is added to the synthetic pre-stack AVA gathers of the training dataset. Synthetic pre-stack AVA gathers with different signal-to-noise ratios are employed to verify the robustness of the proposed method and explore its generalization to field seismic data. By testing MDD-CGANs applied to a 2D elastic parameter model, the inversion results by MDD-CGANs are closer to the true values than those by the limited-data-driven (LDD) CGANs and fully convolutional networks (FCNs). By applying MDD-CGANs to field pre-stack AVA gathers in the Tarim Basin, West China, more accurate and high-resolution results are obtained than those obtained using the conventional pre-stack amplitude-versus-offset (AVO) elastic parameters inversion approach.