Results 21 to 30 of about 1,182 (196)

Efficient Seismic Denoising Transformer with Gradient Prediction and Parameter-Free Attention [PDF]

Jisuanji kexue yu tansuo
Suppression of random noise can effectively improve the signal-to-noise ratio (SNR) of seismic data. In recent years, convolutional neural network (CNN)-based deep learning methods have shown significant performance in seismic data denoising.
GAO Lei, QIAO Haowei, LIANG Dongsheng, MIN Fan, YANG Mei
doaj   +3 more sources

Nonlinear Seismic Signal Denoising Using Template Matching with Time Difference Detection Method [PDF]

Remote Sensing
As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in
Rongwei Xu, Bo Feng, Huazhong Wang, Chengliang Wu, Zhenbo Nie   +4 more
doaj   +2 more sources

End-to-end seismic signals denoising via deep residual convolution and self-attention mechanisms [PDF]

大数据
Denoising of seismic waveform signals is crucial for seismic monitoring and seismological research. To this end, we propose an end-to-end deep learning method for denoising seismic waveforms.
Zhao Botao, Kang Zuheng, He Yayun, Peng Junqing, Zhang Xulong, Qu Xiaoyang, Tan Yipei, Chen Yule, Xiao Chunguang, Wang Jianzong   +9 more
doaj   +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

Denoising Seismic Signal via Resampling Local Applicability Functions [PDF]

, 2022
We propose a novel seismic signal processing approach to efficiently and effectively attenuate seismic random noises. The proposed approach is a generalized seismic noise attenuation solution that can be applied to typical denoising operators.
Liu, Naihao, Xie, Rui, Li, Fangyu, Sun, Fengyuan   +3 more
core   +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

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

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

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