Results 1 to 10 of about 382,825 (168)

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.
Zhenming Peng, Shu Li
exaly   +4 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,
Guangzao Huang
exaly   +3 more sources

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   +3 more sources

Multi-scale dual-path attention network for seismic background noise attenuation [PDF]

Scientific Reports
The background noise in seismic records severely interferes with the extraction of effective reflection events, particularly in complex exploration environments such as deserts.
Li Han, Dongyan Wang, Feng Li
doaj   +2 more sources

Seismic data denoising based on attention dual dilated CNN [PDF]

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   +2 more sources

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

Denoising Method for Seismic Co-Band Noise Based on a U-Net Network Combined with a Residual Dense Block

Applied Sciences, 2023
To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic co ...
Jianxian Cai, Li Wang, Jiangshan Zheng, Zhijun Duan, Ling Li, Ning Chen   +5 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

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

Micromachines, 2022
In view of the fact that the noise in the same frequency band as the useful signal in the MEMS acceleration sensor observation data cannot be effectively removed by traditional filtering methods, a denoising method for strong earthquake signals based on ...
Jiening Xia, Zhigao Chen, Jiang Yang, Yuxiu Chen, Benyan Tan, Zongxuan Wu   +5 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