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.

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.

Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic

2019 ◽  
Vol 219 (2) ◽  
pp. 1281-1299 ◽  
Author(s):  
X T Dong ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Order Statistic ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Low Frequency ◽  
High Order ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
High Order Statistic ◽  
Adaptive Noise

SUMMARY The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernel、patch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.

scholarly journals Noise reduction through joint processing of gravity and gravity gradient data

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. I23-I34 ◽  
Author(s):  
G. Pajot ◽  
O. de Viron ◽  
M. Diament ◽  
M.-F. Lequentrec-Lalancette ◽  
V. Mikhailov
Keyword(s):  
Noise Reduction ◽  
High Frequency ◽  
Random Noise ◽  
Gravity Gradient ◽  
Small Scale ◽  
Frequency Noise ◽  
Gravity Gradiometry ◽  
Physical Constraints ◽  
Simultaneous Inversion ◽  
Marine Gravity

In mineral and oil exploration, gravity gradient data can help to delineate small-scale features that cannot be retrieved from gravity measurements. Removing high-frequency noise while preserving the high-frequency real signal is one of the most challenging tasks associated with gravity gradiometry data processing. We present a method to reduce gravity and gravity gradient data noise when both are measured in the same area, based on a least-squares simultaneous inversion of observations and physical constraints, inferred from the gravity gradient tensor definition and its mathematical properties. Instead of handling profiles individually, our noise-reduction method uses simultaneously measured values of the tensor components and of gravity in the whole survey area, benefiting from all available information. Synthetic examples show that more than half of the random noise can be removed from all tensor components and nearly all the noise from the gravity anomaly without altering the high-frequency information. We apply our method to a set of marine gravity gradiometry data acquired by Bell Geospace in the Faroe-Shetland Basin to demonstrate its power to resolve small-scale features.

scholarly journals Desert seismic signal denoising by 2D compact variational mode decomposition

2019 ◽  
Vol 16 (6) ◽  
pp. 1048-1060 ◽  
Author(s):  
Yue Li ◽  
Linlin Li ◽  
Chao Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Support Functions ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Effective Signal

Abstract Noise suppression and effective signal recovery are very important for seismic signal processing. The random noise in desert areas has complex characteristics due to the complex geographical environment; noise characteristics such as non-stationary, non-linear and low frequency. These make it difficult for conventional denoising methods to remove random noise in desert seismic records. To address the problem, this paper proposes a two-dimensional compact variational mode decomposition (2D-CVMD) algorithm for desert seismic noise attenuation. This model decomposes the complex desert seismic data into an finite number of intrinsic mode functions with specific directions and vibration characteristics. The algorithm introduces binary support functions, which can detect the edge region of the signal in each mode by penalizing the support function through the L1 and total variation (TV) norm. Finally, the signal can be reconstructed by the support functions and the decomposed modes. We apply the 2D-CVMD algorithm to synthetic and real seismic data. The results show that the 2D-CVMD algorithm can not only suppress desert low-frequency noise, but also recover the weak effective signal.

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.

Coherence attribute applications on seismic data in various guises — Part 2

2018 ◽  
Vol 6 (3) ◽  
pp. T531-T541 ◽  
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Bandwidth Extension ◽  
Second Derivatives ◽  
Spectral Components ◽  
Alternative Means ◽  
Seismic Amplitudes ◽  
Reflectivity Inversion ◽  
Derivatives Of ◽  
High Frequency Content

We have previously discussed some alternative means of modifying the frequency spectrum of the input seismic data to modify the resulting coherence image. The simplest method was to increase the high-frequency content by computing the first and second derivatives of the original seismic amplitudes. We also evaluated more sophisticated techniques, including the application of structure-oriented filtering to different spectral components before spectral balancing, thin-bed reflectivity inversion, bandwidth extension, and the amplitude volume technique. We further examine the value of coherence computed from individual spectral voice components, and alternative means of combining three or more such coherence images, providing a single volume for interpretation.

scholarly journals Asymptotic pseudounitary stacking operators

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 1032-1042 ◽  
Author(s):  
Sergey Fomel
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
High Frequency ◽  
Weighting Function ◽  
Inverse Operator ◽  
Adjoint Operator ◽  
Approximate Inverse ◽  
Least Squares Optimization ◽  
Derivatives Of ◽  
Kinematic Properties

Stacking operators are widely used in seismic imaging and seismic data processing. Examples include Kirchhoff datuming, migration, offset continuation, dip moveout, and velocity transform. Two primary approaches exist for inverting such operators. The first approach is iterative least‐squares optimization, which involves the construction of the adjoint operator. The second approach is asymptotic inversion, where an approximate inverse operator is constructed in the high‐frequency asymptotics. Adjoint and asymptotic inverse operators share the same kinematic properties, but their amplitudes (weighting functions) are defined differently. This paper describes a theory for reconciling the two approaches. I introduce a pair of asymptotic pseudounitary operators, which possess both the property of being adjoint and the property of being asymptotically inverse. The weighting function of the asymptotic pseudounitary stacking operators is shown to be completely defined by the derivatives of the operator kinematics. I exemplify the general theory by considering several particular examples of stacking operators. Simple numerical experiments demonstrate a noticeable gain in efficiency when the asymptotic pseudounitary operators are applied for preconditioning iterative least‐squares optimization.

A 2D multistage median filter to reduce random seismic noise

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. V105-V110 ◽  
Author(s):  
Cai Liu ◽  
Yang Liu ◽  
Baojun Yang ◽  
Dian Wang ◽  
Jianguo Sun
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Median Filter ◽  
Random Noise ◽  
Synthetic Data ◽  
Window Size ◽  
Median Filters ◽  
A Value ◽  
Final Data ◽  
Static Corrections

Random noise lowers the S/N of seismic data and decreases the accuracy of dynamic and static corrections, thus degrading final data quality. A 2D multistage median filter (MLM) that effectively reduces the high-frequency random noise can be implemented by applying 1D median filters (MF) in several directions and choosing a value derived from them to output at the center of the 2D window. The choice of window size depends on the intensity of the random noise and the percentage of the input data samples within the window that contain noise. Synthetic data can be used to demonstrate how to choose the window size. The tendency of the method to damage the signal while reducing the noise can be minimized by optimizing window size and by applying two passes with modest-sized windows as opposed to a single pass with a larger window. Results of using the method on prestack and poststack data from the Songliao basin in China demonstrate that the method is effective at both stages.

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
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