A deep learning framework for suppressing prestack seismic random noise without noise-free labels
Random noise attenuation is significant in seismic data processing. Supervised deep learning-based denoising methods have been widely developed and applied in recent years. In practice, it is often time-consuming and laborious to obtain noise-free data for supervised learning. Therefore, we propose...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-07-01
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| Series: | Energy Geoscience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266675922400012X |