Random noise reduction by 3‐D spatial prediction filtering

A common practice in random noise reduction for 2-D data is to use pseudononcausal (PNC) 1-D prediction filters at each temporal frequency. A 1-D PNC filter is a filter that is forced to be two sided by placing a conjugate‐reversed version of a 1-D causal filter in front of itself with a zero between the two. For 3-D data, a similar practice is to solve for two 2-D (causal) one‐quadrant filters at each frequency slice. A 2-D PNC filter is formed by putting a conjugate flipped version of each quadrant filter in a quadrant opposite itself. The center sample of a 2-D PNC filter is zero. This paper suggests the use of 1-D and 2-D noncausal (NC) prediction filters instead of PNC filters for random noise attenuation, where an NC filter is a two‐sided filter solved from one set of normal equations. The number of negative and positive lags in the NC filter is the same. The center sample of the filter is zero. The NC prediction filters are more center loaded than PNC filters. They are conjugate symmetric as PNC filters. Also, NC filters are less sensitive than PNC filters to the size of the gate used in their derivation. They can handle amplitude variations along dip directions better than PNC filters. While a PNC prediction filter suppresses more random noise, it damages more signal. On the other hand, NC prediction filters preserve more of the signal and reject less noise for the same total filter length. For high S/N ratio data, a 2-D NC prediction filter preserves geologic features that do not vary in one of the spatial dimensions. In‐line and cross‐line vertical faults are also well preserved with such filters. When faults are obliquely oriented, the filter coefficients adapt to the fault. Spectral properties of PNC and NC filters are very similar.

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Subband nonstationary noise reduction based on multichannel spatial prediction under reverberant environments

2009 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2009.4959538 ◽

2009 ◽

Author(s):

Masahito Togami ◽

Yohei Kawaguchi ◽

Yasunari Obuchi

Keyword(s):

Noise Reduction ◽

Spatial Prediction ◽

Reverberant Environments

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Random Noise Reduction by FXY Prediction Filtering

Exploration Geophysics ◽

10.1071/eg992051 ◽

1992 ◽

Vol 23 (1-2) ◽

pp. 51-55 ◽

Cited By ~ 19

Author(s):

Michael K. Chase

Keyword(s):

Noise Reduction ◽

Random Noise

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Development and Evaluation of Light-Weight Active Noise Cancellation Earphones

Applied Sciences ◽

10.3390/app8071178 ◽

2018 ◽

Vol 8 (7) ◽

pp. 1178 ◽

Cited By ~ 3

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.

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3-D Post-Stack Random Noise Reduction applied on 3-D seismic data from Tierra del Fuego, Argentina

10.3997/2214-4609-pdb.313.113 ◽

1995 ◽

Author(s):

J. C. Mathewson

Keyword(s):

Noise Reduction ◽

Seismic Data ◽

Random Noise ◽

Tierra Del Fuego

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Random noise reduction by FXY prediction filtering

54th EAEG Meeting ◽

10.3997/2214-4609.201410498 ◽

1992 ◽

Author(s):

M. Chase

Keyword(s):

Noise Reduction ◽

Random Noise

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

Energies ◽

10.3390/en13071763 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1763 ◽

Cited By ~ 1

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.

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HYDROPHONE STREAMER NOISE

Geophysics ◽

10.1190/1.1440466 ◽

1974 ◽

Vol 39 (6) ◽

pp. 781-793 ◽

Cited By ~ 15

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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Projective Noise Reduction Algorithm for Negative Pressure Wave Signal Processing

2008 7th International Pipeline Conference, Volume 1 ◽

10.1115/ipc2008-64126 ◽

2008 ◽

Author(s):

Dongliang Yu ◽

Laibin Zhang ◽

Liang Wei ◽

Zhaohui Wang

Keyword(s):

Noise Reduction ◽

Negative Pressure ◽

Pressure Wave ◽

Random Noise ◽

Background Signal ◽

Long Distance ◽

Noise Component ◽

Wave Signal ◽

Non Linear ◽

Negative Pressure Wave

The appearance of a rupture, leak or damage in the long-distance oil & gas pipeline, which could cause a leak, usually generates a non-linear & chaotic negative pressure wave signal. By properly interpreting the negative pressure wave signature, it is possible to detect a leak along the pipeline. Most traditional noise reduction methods are established based on the linear system, which are not in line with the actual non-linear & chaotic situation. Therefore, the weak negative pressure wave signals, generated by small leaks, are often filtered out and cause false alarm and failure alarm. In order to resolve the problem, this paper uses the non-linear projective algorithm for noise reduction. First, the weak negative pressure wave signal series would be reconstructed using delay coordinates, in the high dimensional phase space, the background signal, the negative pressure wave signal and the noise signal are separated into different sub-spaces. Through the reconstruction of sub-spaces, the weak pressure wave signal can be isolated from the background signal as well as the random noise component reduced.

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