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.

A 1D time-varying median filter for seismic random, spike-like noise elimination

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. V17-V24 ◽  
Author(s):  
Yang Liu ◽  
Cai Liu ◽  
Dian Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Random Noise ◽  
Threshold Value ◽  
Band Pass Filter ◽  
Time Varying ◽  
Noise Elimination ◽  
Pass Filter ◽  
Median Filters

Random noise in seismic data affects the signal-to-noise ratio, obscures details, and complicates identification of useful information. We have developed a new method for reducing random, spike-like noise in seismic data. The method is based on a 1D stationary median filter (MF) — the 1D time-varying median filter (TVMF). We design a threshold value that controls the filter window according to characteristics of signal and random, spike-like noise. In view of the relationship between seismic data and the threshold value, we chose median filters with different time-varying filter windows to eliminate random, spike-like noise. When comparing our method with other common methods, e.g., the band-pass filter and stationary MF, we found that the TVMF strikes a balance between eliminating random noise and protecting useful information. We tested the feasibility of our method in reducing seismic random, spike-like noise, on a synthetic dataset. Results of applying the method to seismic land data from Texas demonstrated that the TVMF method is effective in practice.

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

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.

Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images

2018 ◽  
Vol 11 (3) ◽  
pp. 47-61 ◽  
Author(s):  
Xin-Ming Zhang ◽  
Qiang Kang ◽  
Jin-Feng Cheng ◽  
Xia Wang
Keyword(s):  
Image Restoration ◽  
Performance Improvement ◽  
Adaptive Filtering ◽  
Median Filter ◽  
Window Size ◽  
Salt And Pepper Noise ◽  
Noise Detection ◽  
Median Filters ◽  
Simple Logic ◽  
Salt And Pepper

In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering windows are used for the adoption of candidates for median finding, whatever filtering window size is. Furthermore, the adoption of recursive median filters leads to denoising performance improvement and faster filtering. The simple logic of the proposed algorithm obtains significant milestones on the fidelity of a restored image. Relevant experimental results on subjective visualization and objective digital measure validate the robustness of the proposed filter.

Denoising of aeromagnetic data via the wavelet transform

Geophysics ◽  
2001 ◽  
Vol 66 (6) ◽  
pp. 1793-1804 ◽  
Author(s):  
George E. Leblanc ◽  
William A. Morris
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Random Noise ◽  
Synthetic Data ◽  
Analysis Procedure ◽  
Data Sets ◽  
Aeromagnetic Data ◽  
Wavelet Method ◽  
Southern Alberta ◽  
Geologic Data

Noise has traditionally been suppressed or eliminated in aeromagnetic data sets by the use of Fourier analysis filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they produce undesirable effects when denoising features of moderate to large amplitude and spatial extent. In this study, a new wavelet analysis procedure is presented that substantially reduces the contribution from high‐frequency random noise and noise that is user defined. Applications to both synthetic data and aeromagnetic data from southern Alberta, Canada, show that the wavelet method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data than other methods.

Time-domain inversion of GPR data containing attenuation due to conductive losses

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. K103-K109 ◽  
Author(s):  
Qingyun Di ◽  
Meigen Zhang ◽  
Maioyue Wang
Keyword(s):  
Time Domain ◽  
Ground Penetrating Radar ◽  
Seismic Data ◽  
Random Noise ◽  
Synthetic Data ◽  
The Time Domain ◽  
Ground Penetrating ◽  
Domain Inversion ◽  
Inversion Techniques ◽  
And Inversion

Many seismic data processing and inversion techniques have been applied to ground-penetrating radar (GPR) data without including the wave field attenuation caused by conductive ground. Neglecting this attenuation often reduces inversion resolution. This paper introduces a GPR inversion technique that accounts for the effects of attenuation. The inversion is formulated in the time domain with the synthetic GPR waveforms calculated by a finite-element method (FEM). The Jacobian matrix can be computed efficiently with the same FEM forward modeling procedure. Synthetic data tests show that the inversion can generate high-resolution subsurface velocity profiles even with data containing strong random noise. The inversion can resolve small objects not readily visible in the waveforms. Further, the inversion yields a dielectric constant that can help to determine the types of material filling underground cavities.

Effects of non-reflection events and stationery source locations on virtual seismic reflection images

2020 ◽  
Author(s):  
Ki Kim ◽  
Young-Seok Song ◽  
Joongmoo Byun
Keyword(s):  
Numerical Modeling ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Seismic Data ◽  
Rayleigh Waves ◽  
Seismic Reflection ◽  
Time Window ◽  
Median Filter ◽  
Synthetic Data ◽  
Ricker Wavelet

<p>To notice key obstacles and suggest effective processing methods for virtual reflection images, numerical modeling was performed by the 2-D finite difference method with time and space intervals of 0.2 ms and 1.25 m, respectively. Vertical sources of the Ricker wavelet with a main frequency of 20 Hz were assumed to be detonated independently at five buried locations with intervals of 500 m. Vertical components of the particle velocity were computed at 99 receivers at 10 m depth with intervals of 20 m. Synthetic data show that maximum amplitudes of reflection signals are less than 2% of those of direct Rayleigh waves on an average. This indicates that the non-reflection events should be attenuated as much as possible before correlating traces to compute virtual seismic data. For attenuating both direct and diffracted Rayleigh waves in the synthetic data, a median filter with a time window of a 0.1-s length was effective. Because stationery-phase source locations for virtual reflections concentrate near receiver locations, only common midpoint gathers close to the sources should be used for good virtual stack images.</p>

Deep denoising autoencoder for seismic random noise attenuation

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. V367-V376 ◽  
Author(s):  
Omar M. Saad ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Synthetic Data ◽  
Seismic Signal ◽  
Denoising Autoencoder ◽  
Levels Of Abstraction ◽  
Data Set ◽  
Effective Manner

Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data; following this, the pretrained model is used to denoise the field data set in an unsupervised scheme using a new customized loss function. We have assessed the proposed algorithm based on four synthetic data sets and two field examples, and we compare the results with several benchmark algorithms, such as f- x deconvolution ( f- x deconv) and the f- x singular spectrum analysis ( f- x SSA). As a result, our algorithm succeeds in attenuating the random noise in an effective manner.

Seismic inversion with adaptive edge-preserving smoothing preconditioning on impedance model

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R11-R19 ◽  
Author(s):  
Ronghuo Dai ◽  
Cheng Yin ◽  
Nueraili Zaman ◽  
Fanchang Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Window Size ◽  
Seismic Inversion ◽  
Small Scale ◽  
Inversion Method ◽  
Reservoir Prediction ◽  
Edge Preserving ◽  
Impedance Model

Poststack seismic impedance inversion is an effective approach for reservoir prediction. Due to the sensitivity to noise and the oscillation near the bed boundary, Gaussian distribution constrained seismic inversion is unfavorable to delineate the subtle-reservoir and small-scale geologic features. To overcome this shortcoming, we have developed a new method that incorporates a priori knowledge in the seismic inversion through a preconditioning impedance model using the adaptive edge-preserving smoothing (Ad-EPS) filter. The Ad-EPS filter preconditioned impedance model for a blocky solution makes the formation interfaces and geologic edges more precise and sharper in the inverted impedance results and keeps the inversion procedure robust even if random noise exists in the seismic data. Furthermore, compared with the conventional EPS filter, the Ad-EPS filter is able to resolve thick and thin geologic features through window size scanning, which is used to find the best-fitting window size for each sample to be filtered. The results of numerical examples and real seismic data test indicate that our inversion method can suppress noise to obtain a “blocky” inversion result and preserve small geologic features.

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