Multichannel absorption compensation with a data-driven structural regularization

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. V71-V80 ◽  
Author(s):  
Xiong Ma ◽  
Guofa Li ◽  
Hao Li ◽  
Wuyang Yang
Keyword(s):  
Cost Function ◽  
Structural Characteristic ◽  
Seismic Data ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Frequency Noise ◽  
Characteristic Operator ◽  
High Frequency Noise ◽  
The Cost

Seismic absorption compensation is an important processing approach to mitigate the attenuation effects caused by the intrinsic inelasticity of subsurface media and to enhance seismic resolution. However, conventional absorption compensation approaches ignore the spatial connection along seismic traces, which makes the compensation result vulnerable to high-frequency noise amplification, thus reducing the signal-to-noise ratio (S/N) of the result. To alleviate this issue, we have developed a structurally constrained multichannel absorption compensation (SC-MAC) algorithm. In the cost function of this algorithm, we exploit an [Formula: see text] norm to constrain the reflectivity series and an [Formula: see text] norm to regularize the reflection structural characteristic of the compensation data. The reflection structural characteristic operator, extracted from the observed stacked seismic data, is the core of the structural regularization term. We then solve the cost function of SC-MAC by the alternating direction method of multipliers. Benefiting from the introduction of reflection structure constraint, SC-MAC improves the stability of the compensation result and inhibits the amplification of high-frequency noise. Synthetic and field data examples demonstrate that our proposed method is more robust to random noise and can not only improve the resolution of seismic data, but also maintain the S/N of the compensation seismic data.

Lineament-preserving filtering

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. P1-P8 ◽  
Author(s):  
Saleh Al-Dossary ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Random Noise ◽  
Synthetic Aperture ◽  
Frequency Noise ◽  
Smoothing Process ◽  
Feature Preserving ◽  
Nonlinear Feature ◽  
Derivatives Of ◽  
Analysis Point

Recently developed seismic attributes such as volumetric curvature and amplitude gradients enhance our ability to detect lineaments. However, because these attributes are based on derivatives of either dip and azimuth or the seismic data themselves, they can also enhance high-frequency noise. Recently published structure-oriented filtering algorithms show that noise in seismic data can be removed along reflectors while preserving major structural and stratigraphic discontinuities. In one implementation, the smoothing process tries to select the most homogenous window from a suite of candidate windows containing the analysis point. A second implementation damps the smoothing operation if a discontinuity is detected. Unfortunately, neither of these algorithms preserves thin or small lineaments that are only one voxel in width. To overcome this defect, we evaluate a suite of nonlinear feature-preserving filters developed in the image-processing and synthetic aperture radar (SAR) world and apply them to both synthetic and real 3D dip-and-azimuth volumes of fractured geology from the Forth Worth Basin, USA. We find that the multistage, median-based, modified trimmed-mean algorithm preserves narrow geologically significant features of interest, while suppressing random noise and acquisition footprint.

Application of a Wavelet Extension De-Noising Method in Seismic Data Processing

2012 ◽  
Vol 622-623 ◽  
pp. 1670-1673
Author(s):  
Ye Wu ◽  
Bo Zhang ◽  
Jia Wei
Keyword(s):  
Fourier Transform ◽  
Seismic Data ◽  
High Frequency ◽  
The Other ◽  
Frequency Noise ◽  
Seismic Data Processing ◽  
High Frequency Noise ◽  
Filtering Method ◽  
Components Method ◽  
Coefficient Method

A new wavelet extension de-noising (WED) method is proposed in this paper. The basic principle is derived in detail. We have removed the high frequency noise in seismic data based on the suppressing detail components method, Fourier transform filtering method, WED method and reconstructing the 5th layer approximate coefficient method respectively, and the results show that the WED method can more effectively restrain noise than the other methods.

Case study: Simulation and control of high frequency noise for commercial vehicle cab considering leak

2019 ◽  
Vol 67 (4) ◽  
pp. 315-329
Author(s):  
Rongjiang Tang ◽  
Zhe Tong ◽  
Weiguang Zheng ◽  
Shenfang Li ◽  
Li Huang
Keyword(s):  
High Frequency ◽  
Commercial Vehicle ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Simulation And Control ◽  
And Control

Aerodynamic noise from an asymmetric airfoil with perforated extension plates at the trailing edge

2020 ◽  
pp. 1475472X2097838
Author(s):  
CK Sumesh ◽  
TJS Jothi
Keyword(s):  
High Frequency ◽  
Sound Pressure ◽  
Pore Diameter ◽  
Low Frequency ◽  
Aerodynamic Noise ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Trailing Edge ◽  
High Frequency Noise ◽  
Displacement Thickness

This paper investigates the noise emissions from NACA 6412 asymmetric airfoil with different perforated extension plates at the trailing edge. The length of the extension plate is 10 mm, and the pore diameters ( D) considered for the study are in the range of 0.689 to 1.665 mm. The experiments are carried out in the flow velocity ( U∞) range of 20 to 45 m/s, and geometric angles of attack ( αg) values of −10° to +10°. Perforated extensions have an overwhelming response in reducing the low frequency noise (<1.5 kHz), and a reduction of up to 6 dB is observed with an increase in the pore diameter. Contrastingly, the higher frequency noise (>4 kHz) is observed to increase with an increase in the pore diameter. The dominant reduction in the low frequency noise for perforated model airfoils is within the Strouhal number (based on the displacement thickness) of 0.11. The overall sound pressure levels of perforated model airfoils are observed to reduce by a maximum of 2 dB compared to the base airfoil. Finally, by varying the geometric angle of attack from −10° to +10°, the lower frequency noise is seen to increase, while the high frequency noise is observed to decrease.

Bayesian deconvolution of gamma‐ray logs

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
Author(s):  
Ping Sheng ◽  
Benjamin White ◽  
Balan Nair ◽  
Sandra Kerford
Keyword(s):  
High Frequency ◽  
Gamma Ray ◽  
Simulated Data ◽  
Field Measurements ◽  
Bayesian Optimization ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Data Application ◽  
Gamma Ray Detector ◽  
Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

High frequency noise of MOSFETs. II. Experiments

1998 ◽  
Vol 42 (11) ◽  
pp. 2083-2092 ◽  
Author(s):  
C.H. Chen ◽  
M.J. Deen ◽  
Z.X. Yan ◽  
M. Schroter ◽  
C Enz
Keyword(s):  
High Frequency ◽  
Frequency Noise ◽  
High Frequency Noise

scholarly journals Modeling of low- and high-frequency noise by slow and fast fluctuators

2012 ◽  
Vol 85 (5) ◽  
Author(s):  
Alexander I. Nesterov ◽  
Gennady P. Berman
Keyword(s):  
High Frequency ◽  
Frequency Noise ◽  
High Frequency Noise
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