Results 21 to 30 of about 4,442 (191)

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

Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

IEEE Access, 2022
Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process.
Liang Guo, Renze Luo, Xingyu Li, Yang Zhou, Tuo Juanjuan, Canru Lei   +5 more
doaj   +1 more source

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

Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks

Frontiers in Earth Science, 2021
Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR).
Zhitao Gao, Zhitao Gao, Song Zhang, Jianxian Cai, Jianxian Cai, Li Hong, Li Hong, Jiangshan Zheng   +7 more
doaj   +1 more source

Structure-Preserving Random Noise Attenuation Method for Seismic Data Based on a Flexible Attention CNN

Remote Sensing, 2022
The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of ...
Wenda Li, Tianqi Wu, Hong Liu
doaj   +1 more source

Denoising seismic noise cross correlations [PDF]

Journal of Geophysical Research: Solid Earth, 2009
Seismic noise cross correlations have become a novel way of probing the elastic structure of the Earth without relying on an often highly nonuniform and sporadic distribution of earthquakes. By circumventing this restriction, one can determine the elastic Green's function between any two points where instruments exist.
Baig, Adam M., Campillo, Michel, Brenguier, Florent   +2 more
openaire   +3 more sources

Imaging Domain Seismic Denoising Based on Conditional Generative Adversarial Networks (CGANs)

Energies, 2022
A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface.
Hao Zhang, Wenlei Wang
doaj   +1 more source

Iterative algorithms for total variation-like reconstructions in seismic tomography [PDF]

, 2012
A qualitative comparison of total variation like penalties (total variation, Huber variant of total variation, total generalized variation, ...) is made in the context of global seismic tomography.
A. Beck, A. Chambolle, A. Chambolle, A. Cohen, A. Gholami, C. Ronchi, D. Strong, E. Berg van den, E. Candes, E. Esser, F.J. Herrmann, F.J. Simons, G. Hennenfent, I. Daubechies, I. Loris, I. Loris, J. Trampert, J. Trampert, J. Trampert, K. Bredies, L.I. Rudin, M. Yamagishi, P.J. Huber, S. Mallat, S. Setzer, S.S. Chen, T. Goldstein, T. Lukic, T.F. Chan, X. Zhang, Y.E. Nesterov   +30 more
core   +1 more source

Seismic random noise suppression using improved CycleGAN

Frontiers in Earth Science, 2023
Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-
Shimin Sun, Guihua Li, Renwei Ding, Lihong Zhao, Lihong Zhao, Yujie Zhang, Shuo Zhao, Jinwei Zhang, Junlin Ye   +8 more
doaj   +1 more source