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.

Random noise attenuation by planar mathematical morphological filtering

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V11-V25 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Seismic Exploration ◽  
Noise Attenuation ◽  
Seismic Waveforms ◽  
Morphological Filtering ◽  
Alternative Techniques ◽  
The Difference ◽  
The Relationship

Improving the signal-to-noise ratio (S/N) of seismic data is desirable in many seismic exploration areas. The attenuation of random noise can help to improve the S/N. Geophysicists usually use the differences between signal and random noise in certain attributes, such as frequency, wavenumber, or correlation, to suppress random noise. However, in some cases, these differences are too small to be distinguished. We used the difference in planar morphological scales between signal and random noise to separate them. The planar morphological scale is the information that describes the regional shape of seismic waveforms. The attenuation of random noise is achieved by removing the energy in the smaller morphological scales. We call our method planar mathematical morphological filtering (PMMF). We analyze the relationship between the performance of PMMF and its input parameters in detail. Applications of the PMMF method to synthetic and field post/prestack seismic data demonstrate good performance compared with competing alternative techniques.

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.

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

Simulation Research on Synthesis Algorithm of Directional Illumination Data

2011 ◽  
Vol 403-408 ◽  
pp. 2337-2340
Author(s):  
Shu Cong Liu ◽  
Yan Xing Song ◽  
Jing Song Yang
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Seismic Analysis ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Effective Means ◽  
Seismic Signal ◽  
Geological Structure ◽  
Seismic Exploration ◽  
The Impact

Seismic illumination analysis was an effective means of recognizing and studying the energy distributions in the underground geological structure in seismic data acquisition. Effective seismic illumination analysis to a priori targeted-geological model to identify the energy distribution of seismic waves, can apply to seismic analysis and amplitude compensation analysis. To increase the signal to noise ratio and resolution of seismic data when vibrator seismic exploration, it was necessary to strengthen the energy of a certain direction to get the High-Precision imaging and the best illumination of the target areas.Simulation research were done on single source directional illumination seismic technology, with seismic illumination analysis, and the impact of source number, spacing change on directional illumination seismic technology were also analyzed. Simulation results showed that the directional seismic technology could improved SNR of seismic data, and could be used for seismic signal processing.

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.

scholarly journals Noise Attenuation of Seismic Data via Deep Multiscale Fusion Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yu Sang ◽  
Jinguang Sun ◽  
Dacheng Gao ◽  
Hao Wu
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Medical Image ◽  
Leading Edge ◽  
Great Success ◽  
Noise Attenuation ◽  
Seismic Data Processing ◽  
Signal Features ◽  
Useful Signal ◽  
Seismic Records

Convolutional neural network- (CNN-) based deep learning (DL) architectures have achieved great success in many fields such as remote sensing, medical image processing, and computer vision. Recently, CNN-based models have also been attempted to solve geophysical problems. This paper presents a noise attenuation method of seismic data via a novel deep learning (DL) architecture, namely, deep multiscale fusion network (MSFN). Firstly, we integrate multiscale fusion (MSF) block to adaptively exploit local signal features at different scales from seismic data. And then, a series of stacked MSF blocks are formed into MSFN, which can restore the noisy seismic data effectively and preserve more useful signal information. Furthermore, a comparative study of our method and other leading edge ones is conducted by using synthetic seismic records and the SEG/EAGE salt and overthrust models. The results qualitatively and quantitatively show the capability of our method of achieving higher peak signal-to-noise ratios (PSNRs) while preserving much more useful information, comparing with other methods. Finally, our method is utilized in the real seismic data processing, obtaining satisfactory results.

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