Seismic PP-Wave AVO Inversion Method for VTI Media Based on Double Discriminator Conditional Generative Adversarial Networks

Elastic parameters play pivotal roles in geophysics, with seismic amplitude variation with offset (AVO) inversion being a common method for obtaining the parameters. In contrast to isotropic media, vertical transversely isotropic (VTI) media, which introduce anisotropic parameters to describe geological characteristics, align more closely with field strata. Conducting AVO inversion based on VTI media enhances the accuracy of inverted parameters. Conventional AVO inversion methods typically rely on low-frequency parameters or training samples, which are often generated from well-log data. However, well-log data are usually insufficient, and obtaining accurate anisotropic parameters from well-log data is challenging. These hinder the generation of low-frequency anisotropic parameters or the creation of training samples with anisotropic parameters as labels, thus impacting the accuracy of inverted parameters for VTI media. Addressing these challenges, we construct a double discriminator conditional generative adversarial network (DDCGAN) models under the constraints of the convolution model theory. Building upon the foundation, we propose a seismic AVO inversion method tailored for VTI media. The DDCGANs combine the conditional generative adversarial networks (CGANs), which have superior feature extraction ability, with the well-established convolution model theory, making it suitable for addressing AVO inversion challenges in VTI media. Iterative optimization of the constructed DDCGANs is achieved by building combined loss functions, including errors of elastic parameters and seismic data. Trial calculations using model and field data demonstrate that the proposed method can improve the accuracy of inverted parameters compared to conventional AVO inversion methods, showcasing its feasibility, advancement, and practicality.