Design and Modeling of Adaptive Noise Suppression Systems with Morphological Approach

2017 ◽  
pp. 266-273
Author(s):  
D. Rakov ◽  
J. Thorbeck ◽  
M. Pecheykina
Keyword(s):  
Noise Suppression ◽  
Morphological Approach ◽  
Adaptive Noise ◽  
Suppression Systems

Car Noise Suppression Using Adaptive Noise Canceller with Speech Suppressors

2016 ◽  
Vol 136 (8) ◽  
pp. 1218-1229
Author(s):  
Yuya Honda ◽  
Arata Kawamura ◽  
Youji Iiguni
Keyword(s):  
Noise Suppression ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic

2019 ◽  
Vol 219 (2) ◽  
pp. 1281-1299 ◽  
Author(s):  
X T Dong ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Order Statistic ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Low Frequency ◽  
High Order ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
High Order Statistic ◽  
Adaptive Noise

SUMMARY The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernel、patch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.

Seismic noise cancellation in a geothermal field

Geophysics ◽  
1991 ◽  
Vol 56 (10) ◽  
pp. 1677-1680 ◽  
Author(s):  
D. B. Harris ◽  
S. P. Jarpe ◽  
P. E. Harben
Keyword(s):  
Seismic Noise ◽  
Noise Suppression ◽  
Noise Cancellation ◽  
Geothermal Field ◽  
Monitoring Site ◽  
High Background ◽  
Reflection And Refraction ◽  
Geothermal Fields ◽  
Passive Seismic ◽  
Adaptive Noise

High background seismic noise due to process machinery in production oil or geothermal fields can present a major problem for active seismic studies such as reflection and refraction surveys and passive seismic studies such as microearthquake monitoring. The general noise suppression problem is a difficult one since process noise may be due to a large number of sources distributed over a large region. In some situations, one or a few sources may dominate the noise field locally, presenting an opportunity for noise suppression by cancellation. In this note we describe an application of adaptive noise cancellation (Widrow, et al., 1975) in which we attempt to suppress noise recorded at a primary monitoring site using reference noise recorded at a major nearby noise source.

Novel Retinex algorithm by interpolation and adaptive noise suppression

2012 ◽  
Vol 19 (9) ◽  
pp. 2541-2547 ◽  
Author(s):  
Wu-jing Li ◽  
Bo Gu ◽  
Jiang-tao Huang ◽  
Ming-hui Wang
Keyword(s):  
Noise Suppression ◽  
Retinex Algorithm ◽  
Adaptive Noise

scholarly journals CMOS depth sensor with programmable filter circuits for environment‐adaptive noise suppression

2018 ◽  
Vol 54 (19) ◽  
pp. 1122-1124 ◽  
Author(s):  
H. Seo ◽  
B. Kim ◽  
J.‐H. Chun ◽  
S.‐J. Kim ◽  
J. Choi
Keyword(s):  
Noise Suppression ◽  
Depth Sensor ◽  
Programmable Filter ◽  
Adaptive Noise
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