Neural networks are commonly used for poststack and prestack seismic inversion. With sufficient labeled data, the neural network-based seismic inversion results are more accurate than that use traditional seismic inversion… Click to show full abstract
Neural networks are commonly used for poststack and prestack seismic inversion. With sufficient labeled data, the neural network-based seismic inversion results are more accurate than that use traditional seismic inversion methods. However, in the case of insufficient labeled data, the accuracy of neural networks-based seismic inversion results decreases and is even lower than those based on the traditional inversion methods. In addition, the seismic inversion results based on neural networks generally suffer from lateral discontinuity. It further reduces the accuracy of the inversion results. To tackle these problems, we propose a prestack seismic amplitude variation with offset (AVO) inversion method based on closed-loop multitask conditional Wasserstein generative adversarial network (CMcWGAN), which is a generative adversarial network (GAN)-based AVO inversion method. CMcWGAN enables simultaneous and accurate inversion of
               
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