Multiple removal based on detection and estimation of localized coherent signal

Geophysics ◽  
2003 ◽  
Vol 68 (2) ◽  
pp. 745-750 ◽  
Author(s):  
Wenkai Lu ◽  
Xuegong Zhang ◽  
Yanda Li
Keyword(s):  
Polynomial Approximation ◽  
Seismic Data ◽  
Primary Energy ◽  
Coherent Signals ◽  
Coherent Signal ◽  
Space Domain ◽  
Practical Applications ◽  
Time Space ◽  
Zero Offset ◽  
Velocity Spectra

The removal of multiples without simultaneously distorting primaries is a difficult aspect of demultiple techniques. We present a new demultiple approach based on detecting and estimating localized coherent signals (multiples and primaries) on stacking velocity spectra. The trajectories of localized coherent signals are determined from the velocity and zero‐offset traveltime; amplitude variations with offset (AVO) are modeled from known AVO properties. We estimate primaries and multiples from stacking velocities by polynomial approximation of the amplitude along the hyperbolic path. This procedure lets us predict multiples on near‐offsets using the multiples on other offsets. To further preserve the amplitude of the rebuilt primaries, stronger multiples, which are detected according to a multiple‐to‐primary energy ratio, are estimated and subtracted from the seismic data before estimating the primaries. Because the estimation of primaries and multiples is done locally in the time–space domain, our method is efficient. Comparisons are made between our method and conventional Radon filters. The results of both synthetic and field seismic data show that our method is very promising in practical applications because it can suppress the multiples efficiently while preserving the primary amplitudes well.

3-D coherency filtering

Geophysics ◽  
1997 ◽  
Vol 62 (4) ◽  
pp. 1310-1314 ◽  
Author(s):  
Qing Li ◽  
Kris Vasudevan ◽  
Frederick A. Cook
Keyword(s):  
Seismic Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Three Dimensions ◽  
Coherent Noise ◽  
Coherent Signal ◽  
Frequency Space ◽  
Time Space ◽  
Incoherent Noise

Coherency filtering is a tool used commonly in 2-D seismic processing to isolate desired events from noisy data. It assumes that phase‐coherent signal can be separated from background incoherent noise on the basis of coherency estimates, and coherent noise from coherent signal on the basis of different dips. It is achieved by searching for the maximum coherence direction for each data point of a seismic event and enhancing the event along this direction through stacking; it suppresses the incoherent events along other directions. Foundations for a 2-D coherency filtering algorithm were laid out by several researchers (Neidell and Taner, 1971; McMechan, 1983; Leven and Roy‐Chowdhury, 1984; Kong et al., 1985; Milkereit and Spencer, 1989). Milkereit and Spencer (1989) have applied 2-D coherency filtering successfully to 2-D deep crustal seismic data for the improvement of visualization and interpretation. Work on random noise attenuation using frequency‐space or time‐space prediction filters both in two or three dimensions to increase the signal‐to‐noise ratio of the data can be found in geophysical literature (Canales, 1984; Hornbostel, 1991; Abma and Claerbout, 1995).

Progressive denoising of seismic data via robust noise estimation in dual domains

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. V99-V118
Author(s):  
Yi Lin ◽  
Jinhai Zhang
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Random Noise ◽  
Denoising Method ◽  
Data Set ◽  
Edge Preserving ◽  
Space Domain ◽  
Time Space ◽  
Frequency Components ◽  
Resolution Imaging

Random noise attenuation plays an important role in seismic data processing. Most traditional methods suppress random noise either in the time-space domain or in the transformed domain, which may encounter difficulty in retaining the detailed structures. We have introduced the progressive denoising method to suppress random noise in seismic data. This method estimates random noise at each sample independently by imposing proper constraints on local windowed data in the time-space domain and then in the transformed domain, and the denoised results of the whole data set are gradually improved by many iterations. First, we apply an unnormalized bilateral kernel in time-space domain to reject large-amplitude signals; then, we apply a range kernel in the frequency-wavenumber domain to reject medium-amplitude signals; finally, we can obtain a total estimate of random noise by repeating these steps approximately 30 times. Numerical examples indicate that the progressive denoising method can achieve a better denoising result, compared with the two typical single-domain methods: the [Formula: see text]-[Formula: see text] deconvolution method and the curvelet domain thresholding method. As an edge-preserving method, the progressive denoising method can greatly reduce the random noise without harming the useful signals, especially to those high-frequency components, which would be crucial for high-resolution imaging and interpretations in the following stages.

scholarly journals Study on the Velocity Analysis Method of Low SNR Seismic Data

2021 ◽  
Vol 11 (1) ◽  
pp. 78
Author(s):  
Jianbo He ◽  
Zhenyu Wang ◽  
Mingdong Zhang
Keyword(s):  
Seismic Data ◽  
Fresnel Zone ◽  
Signal To Noise Ratio ◽  
Normal Wave ◽  
Curvature Radius ◽  
Velocity Spectrum ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Zero Offset

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.

Non-stationary predictive filtering for seismic random noise suppression - A tutorial

Geophysics ◽  
2021 ◽  
pp. 1-79 ◽  
Author(s):  
Hang Wang ◽  
Wei Chen ◽  
Weilin Huang ◽  
Shaohuan Zu ◽  
Xingye Liu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Space Dimension ◽  
Noise Suppression ◽  
Random Noise ◽  
Spatial Dimension ◽  
Seismic Data Processing ◽  
Filtering Method ◽  
Time Space ◽  
Stationary Version ◽  
Predictive Filtering

Predictive filtering in the frequency domain is one of the most widely used denoising algorithms in the seismic data processing workflow. Predictive filtering is based on the assumption of linear/planar events in the time-space domain. In traditional predictive filtering method, the predictive filter is fixed across the spatial dimension, which cannot deal with the spatial variation of seismic data well. To handle the curving events, the predictive filter is either applied in local windows or extended to a non-stationary version. The regularized non-stationary autoregression (RNAR) method can be treated as a non-stationary extension of the traditional predictive filtering, where the predictive filter coefficients are variable in different space locations. The highly under-determined inverse problem is solved by shaping regularization with a smoothness constraint in space. We further extend the RNAR method to a more general case, where we can apply more constraints to the filter coefficients according to the features of seismic data. First, apart from the smoothness in space, we also apply a smoothing constraint in frequency, considering the coherency of the coefficients in the frequency dimension. Secondly, we apply a frequency dependent smoothing radius along the space dimension to better take advantage of the non-stationarity of seismic data in the frequency axis, and to better deal with noise. The proposed method is validated via several synthetic and field data examples.

scholarly journals Low-frequency noise suppression for desert seismic data based on a wide inference network

2019 ◽  
Vol 16 (4) ◽  
pp. 801-810
Author(s):  
Yue Li ◽  
Wei Yu ◽  
Chao Zhang ◽  
Baojun Yang
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Seismic Exploration ◽  
Frequency Noise ◽  
Layer By Layer ◽  
Low Frequency Noise ◽  
Time Space ◽  
Manual Adjustment ◽  
Inference Network ◽  
Seismic Records

Abstract The importance of seismic exploration has been recognized by geophysicists. At present, low-frequency noise usually exists in seismic exploration, especially in desert seismic records. This low-frequency noise shares the same frequency band with effective signals. This leads to the limitation or failure of traditional methods. In order to overcome the shortcomings of traditional denoising methods, we propose a novel desert seismic data denoising method based on a Wide Inference Network (WIN). The WIN aims to minimize the error between the prediction and target by residual learning during training, and it can obtain a set of optimal parameters, such as weights and biases. In this article, we construct a high-quality training set for a desert seismic record and this ensures the effective training of a WIN. In this way, each layer of the trained WIN can automatically extract a set of time–space characteristics without manual adjustment. These characteristics are transmitted layer by layer. Finally, they are utilized to extract effective signals. To verify the effectiveness of the WIN, we apply it to synthetic and real desert seismic records, respectively. In addition, we compare WIN with f – x deconvolution, variational mode decomposition (VMD) and shearlet transform. The results show that WIN has the best denoising performance in suppressing low-frequency noise and preserving effective signals.

scholarly journals Coherent Signal Parameter Estimation by Exploiting Decomposition of Tensors

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Long Liu ◽  
Ling Wang ◽  
Yuexian Wang ◽  
Jian Xie ◽  
Zhaolin Zhang
Keyword(s):  
Parameter Estimation ◽  
Algebraic Method ◽  
Coherent Signals ◽  
Coherent Signal ◽  
Signal Parameters ◽  
Key Innovation ◽  
Canonical Polyadic Decomposition ◽  
New Perspective ◽  
Array Aperture ◽  
Value Decomposition

The problem of parameter estimation of coherent signals impinging on an array with vector sensors is considered from a new perspective by means of the decomposition of tensors. Signal parameters to be estimated include the direction of arrival (DOA) and the state of polarization. In this paper, mild deterministic conditions are used for canonical polyadic decomposition (CPD) of the tensor-based signal model; i.e., the factor matrices can be recovered, as long as the matrices satisfy the requirement that at least one is full column rank. In conjoint with the estimation of signal parameters via the algebraic method, the DOAs and polarization parameters of coherent signals can be resolved by virtue of the first and second factor matrices. Hereinto, the key innovation of the proposed approach is that the proposed approach can effectively estimate the coherent signal parameters without sacrificing the array aperture. The superiority of the proposed algorithm is shown by comparing with the algorithms based on higher order singular value decomposition (HOSVD) and Toeplitz matrix. Theoretical and numerical simulations demonstrate the effectiveness of the proposed approach.

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