Improved DUDnCNN-based noise reduction method for seismic data

Zhonghua Ma; Ying Li

doi:10.1117/12.2604563

5 October 2021 Improved DUDnCNN-based noise reduction method for seismic data

Zhonghua Ma, Ying Li

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119111W (2021) https://doi.org/10.1117/12.2604563
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

The DUDnCNN algorithm is a combination of feedforward denoising network and U-Net network with symmetric connections for better feature fusion. Net network with symmetric connections for better feature fusion, while the first proposed improved algorithm changes the network structure and adds asymmetric connections to achieve feature fusion between different layers again. In addition, the improved network with respect to residual learning and different ways of connectivity is also proposed and tested under different noises for training. From the experimental comparison effect, it can be seen that the improved algorithm of DUDnCNN has better applicability in fidelity and noise reduction and also has certain generalization ability, and the algorithm also has the feasibility of further investigation in the field of seismic data fidelity and noise reduction and seismic interpretation.

Citation Download Citation

Zhonghua Ma and Ying Li "Improved DUDnCNN-based noise reduction method for seismic data", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119111W (5 October 2021); https://doi.org/10.1117/12.2604563

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KEYWORDS

Denoising

Image processing

Data modeling

Feature extraction

Network architectures

Neural networks

Image processing algorithms and systems

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