A frequency domain-based study for fluid-borne noise reduction in hydraulic system with simple passive elements

2021 ◽  
Vol 4 (3) ◽  
pp. 203
Author(s):  
Leandro Danes ◽  
Andrea Vacca
Keyword(s):  
Noise Reduction ◽  
Frequency Domain ◽  
Hydraulic System

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhitao Gao ◽  
Song Zhang ◽  
Jianxian Cai ◽  
Li Hong ◽  
Jiangshan Zheng
Keyword(s):  
Noise Reduction ◽  
Frequency Domain ◽  
Seismic Signal ◽  
Signal Denoising ◽  
Seismic Signals ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Deep Network ◽  
Network Time ◽  
Feature Information

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). However, the current convolutional deep network used for seismic signal noise reduction does not make full use of the feature information extracted from all convolution layers in the network, and thus cannot fit the seismic signal with high SNR. To deal with this problem, this paper proposes the DnRDB model, a convolutional deep network time-frequency domain seismic signal denoising model combined with residual dense blocks (RDB). The model is mainly composed of several RDB in series. The input of each convolution layer in each RDB module is formed by the output of all the previous convolution layers. Meanwhile, even if the number of layers is increased, the fusion of the seismic signal features learned by the RDB modules can still achieve full extraction of seismic signals. Furthermore, deepening the model structure by concatenating multiple RDB modules enables further useful feature information to be extracted, which improves the SNR of seismic signals. The DnRDB model was trained and tested using the Stanford Global Seismic Dataset. The experimental results show that the DnRDB model can effectively recover seismic signals and remove various forms of noise. Even in the case of high noise, the denoised signal still has a high SNR. When the DnRDB model is compared with other denoising approaches such as wavelet threshold, empirical mode decomposition, and different deep learning methods, the results indicate that it performs best overall in denoising the same segment of the noisy seismic signal; the denoised signal also has less waveform distortion. Use of the DnRDB model in subsequent seismic signal processing work indicates that it can help the phase recognition algorithm improve the accuracy of seismic recognition through noise reduction.

Sign in / Sign up

Export Citation Format

Share Document