Dual Wasserstein generative adversarial network condition: A generative adversarial network-based acoustic impedance inversion method

Abstract

Deep learning neural networks offer some advantages over conventional methods in acoustic impedance inversion. Because labeled data may be difficult to obtain in realistic field data settings, it can be difficult to obtain high-accuracy inversion results. Some GAN-based acoustic impedance inversion methods have been proposed to solve this problem. However, due to the existence of lateral discontinuity in inversion results of these GAN-based methods, inversion accuracy of these proposals is still not fully satisfactory. Therefore, to tackle the shortcomings of GAN, we propose an acoustic impedance inversion method based on Dual Wasserstein Generative Adversarial Network condition (Dual-WGAN-c). Dual-WGAN-c can perform seismic inversion and forward calculation simultaneously, and take the low-frequency information of acoustic impedance as conditional input to obtain accurate acoustic impedance inversion results in the case of insufficient labeled data. Through a data simulation experiment, compared to GAN-based seismic acoustic impedance inversion methods, Dual-WGAN-c can obtain more accurate inversion results, with high robustness when the seismic data is noisy. Dual-WGAN-c can also obtain accurate results in acoustic impedance inversion of recorded seismic data when one has insufficient labeled well-log data.