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Deep-Learning Full-Waveform Inversion Using Seismic Migration Images
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DOI：10.1109/TGRS.2021.3062688.png)
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摘要
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Data-driven deep-learning full-waveform inversion (DD-DLFWI) can efficiently reconstruct a velocity image of the subsurface from prestack seismic recordings, once the deep-learning (DL) model is well-trained based on the self-designed geological structures and simulated recordings. However, the key problem of this approach is that it usually discards the knowledge about the forward and adjoint operators, which leads to poor reconstruction quality and generalization ability. To mitigate these problems, we have developed a deep-learning full-waveform inversion (DLFWI) approach using seismic migration images. This approach includes two key points. The first key point is that, unlike the conventional DD-DLFWI approach based on the seismic recordings in the common-source data domain, our approach utilizes the reverse time migration (RTM) images of seismic recordings in the common-source image domain as the data engine of the convolutional neural network (CNN) to reconstruct the background velocity. The second key point is that we utilize the iterative neural network architecture to reconstruct the high-resolution velocity model based on the reconstructed background velocity. Specifically, the high-resolution velocity model can be recovered by using the reconstructed velocity model, RTM image, and gradient of regularization term as the input of neural network architecture. Through synthetic experiments with various layered and fault velocity models, we have confirmed that the proposed approach can reconstruct a high-resolution velocity of the subsurface from prestack seismic recordings. Meanwhile, it outperforms the conventional DD-DLFWI approach in terms of reconstruction accuracy, antinoise, and generalization ability.
Keyword:
Image reconstruction
Data models
Neural networks
Iterative methods
Inverse problems
Tools
Electronics packaging
Deep-learning
full-waveform inversion
inverse problem
reverse time migration
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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