Deblending of simultaneous source data using a structure-oriented space-varying median filter

2020 ◽  
Vol 222 (3) ◽  
pp. 1805-1823 ◽  
Author(s):  
Yangkang Chen ◽  
Shaohuan Zu ◽  
Yufeng Wang ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Noisy Data ◽  
Real Data ◽  
Filter Method ◽  
Local Slope ◽  
Median Filters ◽  
Call Structure ◽  
Slope Estimation ◽  
Simultaneous Source

SUMMARY In seismic data processing, the median filter is usually applied along the structural direction of seismic data in order to attenuate erratic or spike-like noise. The performance of a structure-oriented median filter highly depends on the accuracy of the estimated local slope from the noisy data. When local slope contains significant error, which is usually the case for noisy data, the structure-oriented median filter will still cause severe damages to useful energy. We propose a type of structure-oriented median filter that can effectively attenuate spike-like noise even when the local slope is not accurately estimated, which we call structure-oriented space-varying median filter. A structure-oriented space-varying median filter can adaptively squeeze and stretch the window length of the median filter when applied in the locally flattened dimension of an input seismic data in order to deal with the dipping events caused by inaccurate slope estimation. We show the key difference among different types of median filters in detail and demonstrate the principle of the structure-oriented space-varying median filter method. We apply the structure-oriented space-varying median filter method to remove the spike-like blending noise arising from the simultaneous source acquisition. Synthetic and real data examples show that structure-oriented space-varying median filter can significantly improve the signal preserving performance for curving events in the seismic data. The structure-oriented space-varying median filter can also be easily embedded into an iterative deblending procedure based on the shaping regularization framework and can help obtain much improved deblending performance.

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.

Computation of dips and azimuths with weighted structural tensor approach

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. V119-V121 ◽  
Author(s):  
Yi Luo ◽  
Yuchun Eugene Wang ◽  
Nasher M. AlBinHassan ◽  
Mohammed N. Alfaraj
Keyword(s):  
Seismic Data ◽  
Weighting Function ◽  
Noisy Data ◽  
Real Data ◽  
Weight Factor ◽  
Tensor Method ◽  
Instantaneous Phase ◽  
Instantaneous Power ◽  
Structural Tensor ◽  
Data Adaptive

The structural tensor method can be used to compute dips and azimuths (i.e., orientation) encased in seismic data. However, this method may produce erratic and uninterpretable orientations when noisy data are encountered. To overcome this difficulty, we incorporate a data-adaptive weighting function to reformulate the gradient structural tensor. In our experiment, the squared instantaneous power is adopted as the weight factor; this can simplify the computation when the instantaneous phase is used as input. The real data examples illustrate that such a weighting function can produce more interpretable and spatially consistent orientations than conventional approaches.

Data Rectification and Detection of Trend Shifts in Jet Engine Path Measurements Using Median Filters and Fuzzy Logic

2002 ◽  
Vol 124 (4) ◽  
pp. 809-816 ◽  
Author(s):  
R. Ganguli
Keyword(s):  
Detection System ◽  
Median Filter ◽  
Measurement Data ◽  
Noisy Data ◽  
False Alarms ◽  
Linear Filters ◽  
Data Set ◽  
Median Filters ◽  
Data Rectification ◽  
Path Measurement

Filtering methods are explored for removing noise from data while preserving sharp edges that many indicate a trend shift in gas turbine measurements. Linear filters are found to be have problems with removing noise while preserving features in the signal. The nonlinear hybrid median filter is found to accurately reproduce the root signal from noisy data. Simulated faulty data and fault-free gas path measurement data are passed through median filters and health residuals for the data set are created. The health residual is a scalar norm of the gas path measurement deltas and is used to partition the faulty engine from the healthy engine using fuzzy sets. The fuzzy detection system is developed and tested with noisy data and with filtered data. It is found from tests with simulated fault-free and faulty data that fuzzy trend shift detection based on filtered data is very accurate with no false alarms and negligible missed alarms.

Oriented pre-stack inverse Q filtering for resolution enhancements of seismic data

2020 ◽  
Vol 223 (1) ◽  
pp. 488-501
Author(s):  
Guochang Liu ◽  
Chao Li ◽  
Ying Rao ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Attenuation Factor ◽  
Real Data ◽  
Velocity Model ◽  
Seismic Attenuation ◽  
Propagation Path ◽  
Local Slope ◽  
Filtering Method ◽  
Inverse Q Filtering

SUMMARY Seismic attenuation is one of the main factors responsible for degradation of the resolution of seismic data. During seismic wave propagation in attenuation medium, the energy of signal components seriously decreases, especially those with higher frequencies. The seismic attenuation and resolution reduction are generally compensated for with inverse Q filtering in the frequency or time domain. However, the implementation of pre-stack inverse Q filtering is challenging because the traveltime in each layer is not easy to obtain for the pre-stack seismic gather, unless the accurate velocity model is known. In this study, we propose an inverse Q filtering method for the pre-stack seismic gather that uses the local slope and warped mapping to determine the propagation path, and Taylor-expansion-based division is used to stabilize the inversion. The local slope can determine the reflection events with the same ray path, and the inverse warped mapping can transform the attenuation factor from the ${t_0} - p$ (zero-offset traveltime to ray parameter) domain to the $t - x$ (traveltime and offset) domain. The attenuation factor in the ${t_0} - p$ domain is easy to calculate because the traveltimes and Q values in each layer are known. The proposed oriented pre-stack inverse Q filtering method is velocity-independent and suitable for a depth varying Q model. The synthetic and real data examples demonstrated that the method can effectively correct the attenuation and dispersion of seismic waves, and can obtain pre-stack seismic gathers with high resolution.

A Matlab code package for 2D/3D local slope estimation and structural filtering

Geophysics ◽  
2022 ◽  
pp. 1-102
Author(s):  
Hang Wang ◽  
Yunfeng Chen ◽  
Omar M. Saad ◽  
Wei Chen ◽  
Yapo Abolé Serge Innocent Oboué ◽  
...  
Keyword(s):  
Open Source ◽  
Seismic Data ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Local Slope ◽  
Matlab Code ◽  
Slope Estimation ◽  
And Mathematics ◽  
2D And 3D

Local slope is an important attribute that can help distinguish seismic signals from noise. Based on optimal slope estimation, many filtering methods can be designed to enhance the signal-to-noise ratio (S/N) of noisy seismic data. We present an open-source Matlab code package for local slope estimation and corresponding structural filtering. This package includes 2D and 3D examples with two main executable scripts and related sub-functions. All code files are in the Matlab format. In each main script, local slope is estimated based on the well-known plane wave destruction algorithm. Then, the seismic data are transformed to the flattened domain by utilizing this slope information. Further, the smoothing operator can be effectively applied in the flattened domain. We introduce the theory and mathematics related to these programs, and present the synthetic and field data examples to show the usefulness of this open-source package. The results of both local slope estimation and structural filtering demonstrate that this package can be conveniently and effectively applied to the seismic signal analysis and denoising.

Data Rectification and Detection of Trend Shifts in Jet Engine Gas Path Measurements Using Median Filters and Fuzzy Logic

2001 ◽  
Author(s):  
Ranjan Ganguli
Keyword(s):  
Detection System ◽  
Median Filter ◽  
Measurement Data ◽  
Noisy Data ◽  
False Alarms ◽  
Linear Filters ◽  
Data Set ◽  
Median Filters ◽  
Data Rectification ◽  
Path Measurement

Filtering methods are explored for removing noise from data while preserving sharp edges that many indicate a trend shift in gas turbine measurements. Linear filters are found to be have problems with removing noise while preserving features in the signal. The nonlinear hybrid median filter is found to accurately reproduce the root signal from noisy data. Simulated faulty data and fault free gas path measurement data are passed through median filters and health residuals for the data set are created. The health residual is a scalar norm of the gas path measurement deltas and is used to partition the faulty engine from the healthy engine using fuzzy sets. The fuzzy detection system is developed and tested with noisy data and with filtered data. It is found from tests with simulated fault free and faulty data that fuzzy trend shift detection based on filtered data is very accurate with no false alarms and negligible missed alarms.

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.

Impulse Noise Detection and Removal Method Based on Modified Weighted Median

2020 ◽  
Vol 8 (2) ◽  
pp. 38-53
Author(s):  
Ashpreet ◽  
Mantosh Biswas
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Extreme Values ◽  
Median Filter ◽  
Noise Removal ◽  
Filter Method ◽  
Noise Detection ◽  
Median Filters ◽  
Removal Method ◽  
Weighted Median

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.

Enhancing the Performance of One Dimensional Median Filter

2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu
Keyword(s):  
Impulse Noise ◽  
Rank Order ◽  
Median Filter ◽  
Filter Method ◽  
Salt And Pepper Noise ◽  
Edge Preservation ◽  
Word Level ◽  
Look Ahead ◽  
Filter Architecture ◽  
Salt And Pepper

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. U67-U76 ◽  
Author(s):  
Robert J. Ferguson
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Real Data ◽  
Structural Data ◽  
Irregular Sampling ◽  
Data Set ◽  
Near Surface ◽  
Damped Least Squares ◽  
Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

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