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 attention dual dilated CNN [PDF]
Scientific ReportsSeismic 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 +6 more
doaj +4 more sources
Directional adaptive mode total variation for seismic data denoising [PDF]
Scientific ReportsSeismic 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 +4 more
doaj +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, Zhenming Peng
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
Multi-scale dual-path attention network for seismic background noise attenuation [PDF]
Scientific ReportsThe 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
Efficient seismic data denoising via multi-scale attention network with depthwise separable and residual dilated convolutions [PDF]
Scientific ReportsBecause a high signal-to-noise ratio (SNR) is critical in enhancing the accuracy of subsequent processing, noise reduction remains a pivotal challenge in seismic signal processing, especially for complex noise interference scenarios.
Zhenjing Yao +4 more
doaj +2 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 +5 more
doaj +2 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 +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 +3 more
doaj +1 more source