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Broadband Reconstruction of Seismic Data with Generative Recurrent Adversarial Network.pdf (2.18 MB)
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Broadband Reconstruction of Seismic Data with Generative Recurrent Adversarial Network

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preprint
posted on 10.05.2022, 03:09 by wei songwei song, Yaying Song

Wei et al. describe a new method to reconstruct broadband of seismic data by GRAN deep learning. The deep learning neural network is based on an improved network architecture of GAN and the discriminator is to build the loss function by introducing Wasserstein distance to ensure no gradient vanishing at any time. Although synthetic dataset was used in training, the method still gave satisfactory results in the real data processing study and it also showed the importance of multi disciplinary collaboration and data cleaning in deep learning.

Funding

"Basic experiment and frontier theory and method research of geophysical exploration " of CNPC under Grant 2022dq0604

History

Email Address of Submitting Author

songwei@cup.edu.cn

Submitting Author's Institution

The State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249

Submitting Author's Country

China