Coherent Noise Attenuation Methods for Low-Fold Seismic Data

1993 ◽  
Vol 24 (3-4) ◽  
pp. 479-486
Author(s):  
Guy Duncan ◽  
Greg Beresford
Keyword(s):  
Seismic Data ◽  
Noise Attenuation ◽  
Coherent Noise

A procedure for optimally removing localized coherent noise

Geophysics ◽  
1995 ◽  
Vol 60 (1) ◽  
pp. 191-203 ◽  
Author(s):  
A. Frank Linville ◽  
Robert A. Meek
Keyword(s):  
Seismic Data ◽  
Seismic Profile ◽  
Noise Attenuation ◽  
Signal Distortion ◽  
Coherent Noise ◽  
Vertical Seismic Profile ◽  
Seismic Records ◽  
Signal Distortions ◽  
Notch Filtering ◽  
Phase Differences

Primary reflections in seismic records are often obscured by coherent noise making processing and interpretation difficult. Trapped water modes, surface waves, scattered waves, air waves, and tube waves to name a few, must be removed early in the processing sequence to optimize subsequent processing and imaging. We have developed a noise canceling algorithm that effectively removes many of the commonly encountered noise trains in seismic data. All currently available techniques for coherent noise attenuation suffer from limitations that introduce unacceptable signal distortions and artifacts. Also, most of those techniques impose the dual stringent requirements of equal and fine spatial sampling in the field acquisition of seismic data. Our technique takes advantage of characteristics usually found in coherent noise such as being localized in time, highly aliased, nondispersive (or only mildly so), and exhibit a variety of moveout patterns across the seismic records. When coherent noise is localized in time, a window much like a surgical mute is drawn around the noise. The algorithm derives an estimate of the noise in the window, automatically correcting for amplitude and phase differences, and adaptively subtracts this noise from the window of data. This signal estimate is then placed back in the record. In a model and a land data example, the algorithm removes noise more effectively with less signal distortion than does f-k filtering or velocity notch filtering. Downgoing energy in a vertical seismic profile (VSP) with irregular receiver spacing is also removed.

scholarly journals COHERENT NOISE ATTENUATION USING THE WAVELET TRANSFORM ON RADIAL BASIS

2017 ◽  
Vol 34 (4) ◽  
Author(s):  
Lucas José Andrade de Almeida ◽  
Rafael Rodrigues Manenti ◽  
Milton J. Porsani
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Time Domain ◽  
Seismic Data ◽  
Seismic Noise ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Reflection Seismic ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

ABSTRACT. Radial transform rearranges amplitudes of seismic data, from distance-time domain to angle-time domain. Linear events in distance-time domain tend to e sampled as a vertical event in angle-time domain, while seismic...Keywords: reflection seismic, noise attenuation, signal processing, multi-resolution analysis. RESUMO. A transformada radial faz um remapeamento das amplitudes do dado sísmico do domínio espaço tempo para o domínio ângulo-tempo. Eventos lineares no primeiro domínio tendem...Palavras-chave: sísmica de reflexão, atenuação de ruídos, processamento de sinais, análise de multirresolução.

Swell-noise attenuation: A deep learning approach

2019 ◽  
Vol 38 (12) ◽  
pp. 934-942 ◽  
Author(s):  
Xing Zhao ◽  
Ping Lu ◽  
Yanyan Zhang ◽  
Jianxiong Chen ◽  
Xiaoyang Li
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Learning Model ◽  
Noise Model ◽  
Learning Approach ◽  
Data Sets ◽  
Great Success ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Deep Learning Model

Noise attenuation for ordinary images using machine learning technology has achieved great success in the computer vision field. However, directly applying these models to seismic data would not be effective since the evaluation criteria from the geophysical domain require a high-quality visualized image and the ability to maintain original seismic signals from the contaminated wavelets. This paper introduces an approach equipped with a specially designed deep learning model that can effectively attenuate swell noise with different intensities and characteristics from shot gathers with a relatively simple workflow applicable to marine seismic data sets. Three significant benefits are introduced from the proposed deep learning model. First, our deep learning model doesn't need to consume a pure swell-noise model. Instead, a contaminated swell-noise model derived from field data sets (which may contain other noises or primary signals) can be used for training. Second, inspired by the conventional algorithm for coherent noise attenuation, our neural network model is designed to learn and detect the swell noise rather than inferring the attenuated seismic data. Third, several comparisons (signal-to-noise ratio, mean squared error, and intensities of residual swell noises) indicate that the deep learning approach has the capability to remove swell noise without harming the primary signals. The proposed deep learning-based approach can be considered as an alternative approach that combines and takes advantage of both the conventional and data-driven method to better serve swell-noise attenuation. The comparable results also indicate that the deep learning method has strong potential to solve other coherent noise-attenuation tasks for seismic data.

scholarly journals Convolutional sparse coding for noise attenuation in seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. V23-V30
Author(s):  
Zhaolun Liu ◽  
Kai Lu
Keyword(s):  
Sparse Coding ◽  
Seismic Data ◽  
Field Data ◽  
Synthetic Data ◽  
Basis Functions ◽  
High Signal ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Denoising Method ◽  
Data Set

We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training with the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in complex field data. By excluding filters with coherent noise features, our method can further attenuate coherent noise and separate ground roll.

Coherent noise attenuation in the radial trace domain

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1408-1416 ◽  
Author(s):  
David C. Henley
Keyword(s):  
Seismic Data ◽  
Noisy Data ◽  
Data Mapping ◽  
Noise Attenuation ◽  
Practical Application ◽  
Coherent Noise ◽  
Data Set ◽  
Persistent Problem ◽  
Mapping Algorithm ◽  
Better Than

Coherent noise is a persistent problem in seismic imaging, and a number of techniques have been developed to attenuate it. The radial trace (RT) transform, a simple seismic data mapping algorithm, can be used as the basis for a particularly flexible and effective method for attenuating coherent noise on both prestack and poststack seismic data. Described here are the principles and some practical application details for attenuating coherent noise in the RT domain. A comparison between frequency–wavenumber (f–k) and RT domain filtering on a synthetic model is presented, and some of the differences and advantages of RT methods are identified. Next, RT coherent noise attenuation is demonstrated using a set of good‐quality field data; it is then applied to a very noisy data set. The results obtained with this last set prove to be as good as, or better than, those produced using f–k filtering.

Mathematical morphological filtering for linear noise attenuation of seismic data

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. V369-V384 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Dong Zhang ◽  
Yanxin Zhou ◽  
Wencheng Yang ◽  
...  
Keyword(s):  
Coordinate System ◽  
Seismic Data ◽  
Seismic Waves ◽  
Seismic Exploration ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Traditional Methods ◽  
Median Filters ◽  
Useful Signal ◽  
Morphological Filtering

Linear coherent noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use the differences in frequency, wavenumber, or amplitude to separate the useful signal and coherent noise. However, the application of traditional methods is limited or even invalid when the aforementioned differences between useful signal and coherent noise are too small to be distinguished. For this reason, we have managed to develop a new algorithm from the differences in the shape of seismic waves, and thus, introduce mathematical morphological filtering (MMF) into coherent noise attenuation. The morphological operation is calculated in the trace direction of a rotating coordinate system. This rotating coordinate system is along the direction of the trajectory of coherent noise to make the energy of the coherent noise distributed along the horizontal direction. The MMF approach is more effective than mean and median filters in rejecting abnormal values and causes fewer artifacts compared with [Formula: see text]-[Formula: see text] filtering. Our technique requires that coherent noise can be picked successfully. Application of our technique on synthetic and field seismic data demonstrates its successful performance.

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