HYDROPHONE STREAMER NOISE

Geophysics ◽  
1974 ◽  
Vol 39 (6) ◽  
pp. 781-793 ◽  
Author(s):  
M. Schoenberger ◽  
J. F. Mifsud

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.

2015 ◽  
Vol 1092-1093 ◽  
pp. 300-303 ◽  
Author(s):  
Yu Heng Yan ◽  
Yan Song Li

Optical current transformer (OCT) measured current signal which is mixed with strong random noise. The measured readings can’t accurately reflect the value of the measured current. Since the optical current transformer noise inside the band is basically where the measured current signal overlap,we can not use the traditional method to filter it out. This paper describes the measurement principle based on the Faraday effect of optical current transformer and signal to noise characteristics. Considering optical current transformer for low SNR characteristics, and embedded systems do not have the characteristics of a matrix library, we proposed using sequential Kalman filter to improve the real-time output signal to noise ratio. In the measured current for DC and AC conditions,we established an appropriate state space model Kalman filter.,and conduct simulation on matlab. Practice shows that the sequential Kalman filter algorithm can effectively improve the output signal to noise ratio and accuracy.


2011 ◽  
Vol 19 (5) ◽  
pp. 3862 ◽  
Author(s):  
Feng Pan ◽  
Wen Xiao ◽  
Shuo Liu ◽  
FanJing Wang ◽  
Lu Rong ◽  
...  

2015 ◽  
Vol 23 (5) ◽  
pp. 6976 ◽  
Author(s):  
Keigo Kamada ◽  
Yosuke Ito ◽  
Sunao Ichihara ◽  
Natsuhiko Mizutani ◽  
Tetsuo Kobayashi

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

2018 ◽  
Vol 8 (7) ◽  
pp. 1178 ◽  
Author(s):  
Sen Kuo ◽  
Yi-Rou Chen ◽  
Cheng-Yuan Chang ◽  
Chien-Wen Lai

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.


Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.


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