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
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 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
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
DFRSeisNet: Fluctuation-Prior-Regularized Background Noise Attenuation for Seismic Signal Denoising [PDF]
SensorsSeismic exploration has progressively expanded into urban fringe regions and areas with intensive human activities, where anthropogenic interference has increased markedly, aggravating the background noise problem and substantially affecting the accuracy
Fei Deng +3 more
doaj +2 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.
Zhenming Peng, Shu Li
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
Dictionary learning with convolutional structure for seismic data denoising and interpolation
Geophysics, 2021 Seismic data inevitably suffers from random noise and missing traces in field acquisition. This limits the utilization of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained remarkable success in seismic data denoising and interpolation.
Yangkang Chen +2 more
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 +5 more
doaj +3 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