Noise attenuation by Venturi tubes

1994 ◽  
Vol 95 (5) ◽  
pp. 2933-2933
Author(s):  
A. Selamet
Keyword(s):  
Noise Attenuation

scholarly journals Improved shift-invariant sparse coding for noise attenuation of magnetotelluric data

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Guang Li ◽  
Xiaoqiong Liu ◽  
Jingtian Tang ◽  
Juzhi Deng ◽  
Shuanggui Hu ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Noise Attenuation ◽  
Magnetotelluric Data

Deep learning for denoising

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. V333-V350 ◽  
Author(s):  
Siwei Yu ◽  
Jianwei Ma ◽  
Wenlong Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Graphics Processing Unit ◽  
Random Noise ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Noise Attenuation ◽  
Optimal Parameters ◽  
Training Set ◽  
Unknown Variance

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the inputs are the raw data sets and the corresponding outputs are the desired clean data. After the completion of training, the deep-learning (DL) method achieves adaptive denoising with no requirements of (1) accurate modelings of the signal and noise or (2) optimal parameters tuning. We call this intelligent denoising. We have used a convolutional neural network (CNN) as the basic tool for DL. In random and linear noise attenuation, the training set is generated with artificially added noise. In the multiple attenuation step, the training set is generated with the acoustic wave equation. The stochastic gradient descent is used to solve the optimal parameters for the CNN. The runtime of DL on a graphics processing unit for denoising has the same order as the [Formula: see text]-[Formula: see text] deconvolution method. Synthetic and field results indicate the potential applications of DL in automatic attenuation of random noise (with unknown variance), linear noise, and multiples.

Nonstationary signal inversion based on shaping regularization for random noise attenuation

2020 ◽  
Vol 17 (3) ◽  
pp. 432-442
Author(s):  
Wu-Yang Yang ◽  
Wei Wang ◽  
Guo-Fa Li ◽  
Xin-Jian Wei ◽  
Wan-Li Wang ◽  
...  
Keyword(s):  
Random Noise ◽  
Noise Attenuation ◽  
Nonstationary Signal ◽  
Random Noise Attenuation ◽  
Signal Inversion
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