Open AccessDeep learning-based noise reduction for seismic data
Author(s) -
Ying Li,
Zhonghua Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012011
Subject(s) - noise reduction , reduction (mathematics) , noise (video) , computer science , artificial neural network , algorithm , noise measurement , gaussian noise , data reduction , artificial intelligence , data mining , mathematics , geometry , image (mathematics)
An improved noise reduction algorithm based on feedforward denoising neural network (DnCNN) is proposed for the noise removal problem of noisy seismic data. The previous DnCNN originally used for noise reduction of seismic data had the problem of large network depth and thus reduced training efficiency. The improved DnCNN algorithm was first proposed for the noise reduction of natural data sets, and this paper applies the algorithm to the noise reduction of seismic data after adjusting the relevant parameters. The analysis and comparison of the experimental results show that the DUDnCNN algorithm can remove noise with high efficiency, and the algorithm has certain feasibility and significance for further research in seismic data noise reduction.