scholarly journals Noise Reduction Methods for Detecting Impulses in Seismic Data.

1997 ◽  
Author(s):  
Cary Cox ◽  
Richard Lewis
Keyword(s):  
Noise Reduction ◽  
Seismic Data ◽  
Reduction Methods

Noise reduction in seismic data using Fourier correction coefficient filtering

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1617-1627 ◽  
Author(s):  
Douglas Alsdorf
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Seismic Data ◽  
Wave Number ◽  
Correction Coefficient ◽  
Band Pass Filter ◽  
Data Set ◽  
Pass Filter ◽  
Phase Angle Difference ◽  
Reduction Methods

The correlation coefficient between two frequency (or two wave number) componets equals the cosine of their phase‐angle difference. This relation can be exploited to build a filter that separates noise from signal in seismic data in either the F‐X or F-K domain (termed “correlation coefficient filtering”). To implement this filter, seismic data are first divided to form two subsets that are then compared using the cosine function. Signal is defined as the correlative frequencies (or wavelengths) while noncorrelative energy is attributed to noise. Depending on the application, appropriate subsets may consist of (1) groups of adjacent traces or (2) low‐fold stacks created from differing shot gathers. When comparing adjacent traces [i.e., (1)], the correlation coefficient filter combines both phase and dip information and assumes that reflections advance relatively little in time across traces and less than the noise. Correlation coefficient filtering of low‐fold stacks [i.e., (2)] does not depend on dip. Reflections are assumed to be present in both subsets whereas the noise is found only in one data set. Hence, the reflections are correlative and the noise is noncorrelative. In either case, the filter reduces linearly dipping coherent energy, ground roll, and randomly occurring noise bursts while generally maintaining signal integrity. A primary advantage of this filter is its simplicity. It is implemented much like a simple band‐pass filter, thus requiring much less parameterization than alternative noise‐reduction methods.

scholarly journals On the Running Noise of Toothed Belt Drive : 2nd Report, Influence of Running Condition and Some Noise Reduction Methods

1971 ◽  
Vol 37 (293) ◽  
pp. 203-211
Author(s):  
Aizoh KUBO ◽  
Toshiaki ANDO ◽  
Susumu SATO ◽  
Toshio AIDA ◽  
Takeshi HOSHIRO
Keyword(s):  
Noise Reduction ◽  
Belt Drive ◽  
Toothed Belt ◽  
Reduction Methods

COMPARISON OF MONOPOLE AND DIPOLE NOISE REDUCTION METHODS

2020 ◽  
Vol 4 (67) ◽  
pp. 153-160
Author(s):  
Oleg I. Polivaev ◽  
◽  
Alexey N. Kuznetsov ◽  
Dmitriy Yu. Terekhov ◽  
Viktor V. Trufanov ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Reduction Methods

Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter

2018 ◽  
Vol 67 (1) ◽  
pp. 123-131 ◽  
Author(s):  
Haitao Ma ◽  
Zebin Qian ◽  
Yue Li ◽  
Hongbo Lin ◽  
Dan Shao ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Seismic Data ◽  
Bandpass Filter ◽  
Spectral Kurtosis

Noise Reduction During Acoustic Monitoring of Laser Welding of High-Strength Steels

2009 ◽  
Author(s):  
Wei Huang ◽  
Radovan Kovacevic
Keyword(s):  
Laser Welding ◽  
Noise Reduction ◽  
Acoustic Signal ◽  
Welding Process ◽  
High Strength Steels ◽  
High Strength ◽  
Weld Penetration ◽  
Acoustic Signatures ◽  
Reduction Methods ◽  
Laser Welding Process

During the laser welding process of high-strength steels, different defects, such as a partial weld penetration, spatters, and blow-through holes could be present. In order to detect the presence of defects and achieve a quality control, acoustic monitoring based on microphones is applied to the welding process. As an effective sensor to monitor the laser welding process, however, the microphone is greatly limited by intensive noise existing in the complex industrial environment. In this paper, in order to acquire a clean acoustic signal from the laser welding process, two noise reduction methods are proposed: one is the spectral subtraction method based on one microphone and the other one is the beamforming based on a microphone array. By applying these two noise reduction methods, the quality of the acoustic signal is enhanced, and the acoustic signatures are extracted both in the time domain and frequency domain. The analysis results show that the extracted acoustic signatures can well indicate the different weld penetration states and they can also be used to study the internal mechanisms of the laser-material interaction.

scholarly journals Improved Turbulence Profiling with Field-Adapted Acoustic Doppler Velocimeters Using a Bifrequency Doppler Noise Suppression Method

2008 ◽  
Vol 25 (3) ◽  
pp. 452-463 ◽  
Author(s):  
D. Hurther ◽  
U. Lemmin
Keyword(s):  
Noise Reduction ◽  
Carrier Frequency ◽  
Noise Suppression ◽  
Isotropic Turbulence ◽  
Field Studies ◽  
Sample Volume ◽  
Grid Turbulence ◽  
Electronic Circuitry ◽  
Reduction Methods ◽  
Velocity Spectra

Abstract A novel noise reduction method and corresponding technique are presented for improving turbulence measurements with acoustic Doppler velocimeters (ADVs) commonly used in field studies of coastal and nearshore regions, rivers, lakes, and estuaries. This bifrequency method is based on the decorrelation of the random and statistically independent Doppler noise terms contained in the Doppler signals at two frequencies. It is shown through experiments in an oscillating grid turbulence (OGT) tank producing diffusive isotropic turbulence that a shift in carrier frequency of less than 10% is sufficient to increase the resolved frequency range by a decade in the turbulent velocity spectra. Over this spectral range, the slope of the velocity spectra agrees well with the universal inertial range value of −5/3. The limit due to spatial averaging effects over the sample volume can be determined from the abrupt deviation of the spectral slope from the −5/3 value. As a result, the relative error of the turbulent intensity estimate and the turbulent kinetic energy (TKE) dissipation rate, measured by two different methods, does not exceed 10% in the case of isotropic turbulence. Furthermore, the bifrequency method allows accurate estimates of the turbulent microscales as shown by the good agreement of the ratio between the Taylor and Kolmogorov microscales and an Re1/4t power law. Compared to previous Doppler noise reduction methods (Garbini et al.), an increase in time resolution by a factor of 4 is achieved. The proposed method also avoids the loss of TKE energy contained in isotropic flow structures of size equal to and smaller than the sample volume. Different from Doppler noise methods proposed by Hurther and Lemmin and Blanckaert and Lemmin, this method does not require additional hardware components, electronic circuitry, or sensors because the redundant instantaneous velocity field information is captured with the same transducer. The required shift in carrier frequency is small enough for the bifrequency method to be easily implemented in commercial ADVs.

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