Results 1 to 10 of about 3,808 (141)

Residual Learning of Cycle-GAN for Seismic Data Denoising

IEEE Access, 2021
Random noise attenuation has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. This paper proposes a cycle-GAN denoising framework
Wenda Li
exaly   +4 more sources

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Energies, 2020
Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li, Xi Yang, Yuwei Cai
exaly   +3 more sources

A Denoising Method for Seismic Data Based on SVD and Deep Learning

Applied Sciences (Switzerland), 2022
When reconstructing seismic data, the traditional singular value decomposition (SVD) denoising method has the challenge of difficult rank selection. Therefore, we propose a seismic data denoising method that combines SVD and deep learning. In this method,
Guoli Ji
exaly   +3 more sources

SeisDeNet: an intelligent seismic data Denoising network for the internet of things

Journal of Cloud Computing: Advances, Systems and Applications, 2023
Deep learning (DL) has attracted tremendous interest in various fields in last few years. Convolutional neural networks (CNNs) based DL architectures have been successfully applied in computer vision, medical image processing, remote sensing, and many ...
Yu Sang, Yanfei Peng, Mingde Lu, Chen Zhao, Liquan Li, Tianjiao Ma   +5 more
doaj   +2 more sources

Seismic data denoising based on attention dual dilated CNN

Scientific Reports
Seismic data denoising is essential for accurate seismic-exploration data processing and interpretation. Traditional noise suppression methods often result in the loss of critical signals, affecting subsurface structure characterization.
Haixia Hu, Youhua Wei, Hui Chen, Xingan Fu, Ji Zhang, Quan Wang, Shiwei Cai   +6 more
doaj   +3 more sources

Directional adaptive mode total variation for seismic data denoising

Scientific Reports
Seismic noise attenuation is a critical task in geophysical data processing. However, addressing directional features while preserving the curvilinear nature of complex seismic events remains a significant challenge.
Tara P. Banjade, Cong Zhou, Yaxin Yang, Hongxing Li, Juzhi Deng   +4 more
doaj   +3 more sources

An Alternative Adaptive Method for Seismic Data Denoising and Interpolation [PDF]

Mathematical Problems in Engineering, 2020
Seismic data denoising and interpolation are generally essential steps for reflection processing and imaging workflow especially for the complex surface geologic conditions and the irregular acquisition field area. The rank-reduction method is a valid way for the attenuation of random noise and data interpolation by selecting the suitable threshold, i ...
Zilin Lu, Nuan Xia, Liang Sun, Wenxing Xu, Guangcheng Zhang, Haiyue Dou, Qifeng Jiang   +6 more
openaire   +1 more source

A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression

Remote Sensing, 2023
Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated.
Haixia Zhao, You Zhou, Tingting Bai, Yuanzhong Chen   +3 more
doaj   +1 more source

Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis

Frontiers in Earth Science, 2023
The signal-to-noise ratio (SNR) of seismic data is the key to seismic data processing, and it also directly affects interpretation of seismic data results.
Guangde Zhang, Huaibang Zhang, Li You, Yuyong Yang, Huailai Zhou, Bohan Zhang, Wujin Chen, Liyuan Liu   +7 more
doaj   +1 more source

Prestack seismic random noise attenuation using the wavelet-inspired invertible network with atrous convolutions spatial pyramid

Frontiers in Earth Science, 2023
Convolutional Neural Network (CNN) is widely used in seismic data denoising due to its simplicity and effectiveness. However, traditional seismic denoising methods based on CNN ignore multi-scale features of seismic data in the wavelet domain.
Liangsheng He, Liangsheng He, Hao Wu, Hao Wu, Xiaotao Wen, Xiaotao Wen   +5 more
doaj   +1 more source