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AVO Inversion Based on Closed-Loop Multitask Conditional Wasserstein Generative Adversarial Network
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DOI：10.1109/TGRS.2023.3260908.png)
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Abstract
En 中文
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 P-wave velocity (V p), S-wave velocity (Vs), and density (rho). Moreover, it uses the low-frequency information of elastic parameters as a conditional input to alleviate the problem of lateral discontinuity in inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on the traditional AVO inversion methods. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than the traditional methods. CMcWGAN can also obtain reasonable AVO inversion results in field seismic angle gather data. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on the traditional AVO inversion method. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than the traditional method. CMcWGAN can also get reasonable AVO inversion results in field seismic angle gather data.
Keywords:
Generative adversarial networks
Multitasking
Feature extraction
Robustness
Convolutional neural networks
Impedance
Convolution
Amplitude variation with offset (AVO) inversion
closed-loop structure
conditional generative adversarial network (cGAN)
Wasserstein generative adversarial network (WGAN)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W