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Seismic Data Denoising and Interpolation Using Deep Learning
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 82nd EAGE Annual Conference & Exhibition, Oct 2021, Volume 2021, p.1 - 5
Abstract
Summary
Seismic data recovery, including noise removal and interpolation, is virtual to improve data quality. We present a modified CBD-RDN network to remove noise and improve resolution simultaneously. As the performance of neural network is heavily influenced by the quality and diversity of data, we introduce two strategies, consistence of frequency bands and data augmentation. Numerical experiments on synthetic and field seismic data indicate that our method preserves more subtle features compared with traditional methods.
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