Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

2020 ◽  
Vol 17 (9) ◽  
pp. 1637-1641
Author(s):  
Jicheng Liu ◽  
Ya Gu ◽  
Yongxin Chou ◽  
Jianfei Gu
Keyword(s):  
Noise Reduction ◽  
Random Noise ◽  
Adaptive Threshold ◽  
Shearlet Transform ◽  
Directional Characteristics

scholarly journals Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71374-71386 ◽  
Author(s):  
Wen-Long Hou ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang ◽  
Meng-Di Deng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Random Noise ◽  
Shearlet Transform ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

13‐4: Adaptive Threshold Method for Dither Noise Reduction in Response Time Acceleration for 8K LCD TV

2021 ◽  
Vol 52 (1) ◽  
pp. 161-164
Author(s):  
Ho-Joon Chung ◽  
Hong-Ki Kwon ◽  
Jin-Yong Park ◽  
Tae-Woo Kim ◽  
Hyeon-Su Park ◽  
...  
Keyword(s):  
Response Time ◽  
Noise Reduction ◽  
Adaptive Threshold ◽  
Threshold Method

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

Random Noise Reduction by FXY Prediction Filtering

1992 ◽  
Vol 23 (1-2) ◽  
pp. 51-55 ◽  
Author(s):  
Michael K. Chase
Keyword(s):  
Noise Reduction ◽  
Random Noise

scholarly journals Development and Evaluation of Light-Weight Active Noise Cancellation Earphones

2018 ◽  
Vol 8 (7) ◽  
pp. 1178 ◽  
Author(s):  
Sen Kuo ◽  
Yi-Rou Chen ◽  
Cheng-Yuan Chang ◽  
Chien-Wen Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Random Noise ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Experimental Results ◽  
Path Model ◽  
Light Weight ◽  
Active Noise ◽  
Natural Sound

This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR’s ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance.

scholarly journals Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1763 ◽  
Author(s):  
Haiqing Liu ◽  
Zhiqiao Li ◽  
Yuancheng Li
Keyword(s):  
Power Consumption ◽  
Noise Reduction ◽  
Random Noise ◽  
Imbalanced Data ◽  
Gradient Boosting ◽  
Data Set ◽  
Detection Model ◽  
Adversarial Network ◽  
Consumption Data ◽  
The Impact

In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing.

HYDROPHONE STREAMER NOISE

Geophysics ◽  
1974 ◽  
Vol 39 (6) ◽  
pp. 781-793 ◽  
Author(s):  
M. Schoenberger ◽  
J. F. Mifsud
Keyword(s):  
Noise Reduction ◽  
Near Field ◽  
Random Noise ◽  
Original System ◽  
Coherent Noise ◽  
Signal Ratio ◽  
Motion Sensitivity ◽  
Noise Characteristics ◽  
Noise Ratio ◽  
Boat Speed

Experiments were performed to determine the noise characteristics of a hydrophone streamer that had incorporated a number of noise reduction features. In the original system, the channels to which the depth‐controller birds were attached were 3 to 4 times noisier than nonbird channels. Fortunately, the bird noise is near‐field and is eliminated simply by increasing bird/hydrophone separation to 9 ft. On this cable, no other discrete noise sources are evident. The boat, propulsion system, lead‐in cable, tail buoy, and ambient sea conditions (moderate seas) do not generate significant noise at towing speeds above 5 knots. The noise on individual hydrophones not near birds is mainly random with only a small coherent component traveling horizontally through the water from the direction of the boat. However, since the 145-ft hydrophone arrays of 20 detectors are much more effective in reducing random noise than coherent noise, the array output consists of approximately equal portions of each. A twofold decrease in the total noise‐to‐signal ratio would result from doubling the array length (to 290 ft) while maintaining the same hydrophone density. This would result in a four to fivefold decrease in the coherent noise‐to‐signal ratio and a 30 percent decrease in the random noise‐to‐signal ratio. Additional noise reduction would result from increasing the hydrophone density and decreasing the motion sensitivity of the hydrophones. (The streamer hydrophones are not the motion canceling type.) At a towing speed of 5.3 knots, the noise level recorded on an array (not near a bird) is equivalent to pressures of 1 μbar. In normal operations with an 8-gun sleeve exploder source, a stacked section signal‐to‐towing noise ratio of 3 was obtained at 3.0 sec. However, the towing noise increases as the cube of the boat speed, and the S/N ratio would decrease by a factor of 11 if the boat speed were doubled. Conversely, decreasing the boat speed by 18 percent would double the signal‐to‐towing noise ratio.

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