Ground-roll attenuation using curvelet downscaling

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. V185-V195 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Mauricio Sacchi
Keyword(s):  
Seismic Data ◽  
Curvelet Transform ◽  
Low Frequencies ◽  
High Frequencies ◽  
Seismic Records ◽  
Mask Function ◽  
Ground Roll ◽  
Least Squares Optimization ◽  
Seismic Reflections ◽  
Content Information

We have developed a ground-roll attenuation strategy for seismic records that adopts the curvelet transform. The curvelet transform decomposes the seismic events based on their dip and frequency content information. The curvelet panels that contain only either reflection or ground-roll energy can be used to alter the curvelet panels with mixed reflection and ground-roll energies. We build a curvelet-domain mask function from the ground-roll-free curvelet coefficients (high frequencies) and downscale it to the ground-roll-contaminated curvelet coefficients (low frequencies). The mask function is used inside a least-squares optimization scheme to preserve the seismic reflections and attenuate the ground roll. Synthetic and real seismic data examples show the application of the proposed ground-roll attenuation method.

A method of ground‐roll elimination

Geophysics ◽  
1988 ◽  
Vol 53 (7) ◽  
pp. 894-902 ◽  
Author(s):  
Ruhi Saatçilar ◽  
Nezihi Canitez
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Wave Motion ◽  
Frequency Interval ◽  
Vertical Resolution ◽  
Matched Filters ◽  
One Dimensional ◽  
Frequency Bands ◽  
Ground Roll ◽  
Seismic Reflections

Amplitude‐ and frequency‐modulated wave motion constitute the ground‐roll noise in seismic reflection prospecting. Hence, it is possible to eliminate ground roll by applying one‐dimensional, linear frequency‐modulated matched filters. These filters effectively attenuate the ground‐roll energy without damaging the signal wavelet inside or outside the ground roll’s frequency interval. When the frequency bands of seismic reflections and ground roll overlap, the new filters eliminate the ground roll more effectively than conventional frequency and multichannel filters without affecting the vertical resolution of the seismic data.

Multidimensional de-aliased Cadzow reconstruction of seismic records

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. A1-A5 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Mauricio Sacchi
Keyword(s):  
Seismic Data ◽  
Interpolation Method ◽  
Hankel Matrix ◽  
Real Data ◽  
Low Frequencies ◽  
Frequency Space ◽  
Reduced Rank ◽  
Seismic Records ◽  
Text Prediction ◽  
Eigen Decomposition

We tested a strategy for beyond-alias interpolation of seismic data using Cadzow reconstruction. The strategy enables Cadzow reconstruction to be used for interpolation of regularly sampled seismic records. First, in the frequency-space ([Formula: see text]) domain, we generated a Hankel matrix from the spatial samples of the low frequencies. To perform interpolation at a given frequency, the spatial samples were interlaced with zero samples and another Hankel matrix was generated from the zero-interlaced data. Next, the rank-reduced eigen-decomposition of the Hankel matrix at low frequencies was used for beyond-alias preconditioning of the Hankel matrix at a given frequency. Finally, antidiagonal averaging of the conditioned Hankel matrix produced the final interpolated data. In addition, the multidimensional extension of the proposed algorithm was explained. The proposed method provides a unifying thread between reduced-rank Cadzow reconstruction and beyond alias [Formula: see text] prediction error interpolation. Synthetic and real data examples were provided to examine the performance of the proposed interpolation method.

A fast and simple method of spectral enhancement

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. V75-V80 ◽  
Author(s):  
Muhammad Sajid ◽  
Deva Ghosh
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Difference Operators ◽  
Frequency Noise ◽  
Simple Method ◽  
Low Frequencies ◽  
High Frequencies ◽  
Algorithm Comparison ◽  
Spectral Enhancement ◽  
Bed Thickness

The ability to resolve seismic thin beds is a function of the bed thickness and the frequency content of the seismic data. To achieve high resolution, the seismic data must have broad frequency bandwidth. We developed an algorithm that improved the bandwidth of the seismic data without greatly boosting high-frequency noise. The algorithm employed a set of three cascaded difference operators to boost high frequencies and combined with a simple smoothing operator to boost low frequencies. The output of these operators was balanced and added to the original signal to produce whitened data. The four convolutional operators were quite short, so the algorithm was highly efficient. Synthetic and real data examples demonstrated the effectiveness of this algorithm. Comparison with a conventional whitening algorithm showed the algorithm to be competitive.

Seismic data interpolation and denoising in the frequency-wavenumber domain

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V71-V80 ◽  
Author(s):  
Mostafa Naghizadeh
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Real Data ◽  
Sampled Data ◽  
Frequency Range ◽  
Unified Approach ◽  
Low Frequencies ◽  
Time Space ◽  
Least Squares Fit ◽  
Mask Function

I introduce a unified approach for denoising and interpolation of seismic data in the frequency-wavenumber ([Formula: see text]) domain. First, an angular search in the [Formula: see text] domain is carried out to identify a sparse number of dominant dips, not only using low frequencies but over the whole frequency range. Then, an angular mask function is designed based on the identified dominant dips. The mask function is utilized with the least-squares fitting principle for optimal denoising or interpolation of data. The least-squares fit is directly applied in the time-space domain. The proposed method can be used to interpolate regularly sampled data as well as randomly sampled data on a regular grid. Synthetic and real data examples are provided to examine the performance of the proposed method.

COLOR SONAGRAMS: A NEW DIMENSION IN SEISMIC DATA INTERPRETATION

Geophysics ◽  
1971 ◽  
Vol 36 (6) ◽  
pp. 1074-1098 ◽  
Author(s):  
A. H. Balch
Keyword(s):  
Seismic Data ◽  
Computer Graphic ◽  
Data Interpretation ◽  
Seismic Events ◽  
Time Varying ◽  
Light Spectrum ◽  
Seismic Section ◽  
Frequency Spectra ◽  
Low Frequencies ◽  
High Frequencies

We have developed a computer‐graphic‐photographic system which uses color mimicry to display the frequency spectra of seismic events simultaneously with their time‐varying waveforms. Mimicking the visible light spectrum, we have used red for the low frequencies and violet for the highs. The output of our system is a variable‐area‐wiggle‐trace seismic cross‐section. The waveforms are the same as those on a conventional section; however, the variable‐area part of the section appears in color. The color represents the frequency spectrum of the wavelets. Lateral changes in rock attenuation show up as color shifts on this type of display. Faults often stand out as interrupted color bands. Fault diffractions sometimes have a characteristic color signature. The cancellation of high frequencies due to misalignment of events on constant‐velocity stacks can show up in color. Loss of high frequencies due to slight lateral changes in moveout velocity, and consequent trace misalignment, is often indicated by a shift toward red on a color seismic section.

Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB189-WB202 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Mauricio D. Sacchi
Keyword(s):  
Seismic Data ◽  
Interpolation Method ◽  
Curvelet Transform ◽  
Real Data ◽  
Interpolation Scheme ◽  
Free Data ◽  
First Pass ◽  
Mask Function ◽  
To Come ◽  
Hierarchical Scale

We propose a robust interpolation scheme for aliased regularly sampled seismic data that uses the curvelet transform. In a first pass, the curvelet transform is used to compute the curvelet coefficients of the aliased seismic data. The aforementioned coefficients are divided into two groups of scales: alias-free and alias-contaminated scales. The alias-free curvelet coefficients are upscaled to estimate a mask function that is used to constrain the inversion of the alias-contaminated scale coefficients. The mask function is incorporated into the inversion via a minimum norm least-squares algorithm that determines the curvelet coefficients of the desired alias-free data. Once the alias-free coefficients are determined, the curvelet synthesis operator is used to reconstruct seismograms at new spatial positions. The proposed method can be used to reconstruct regularly and irregularly sampled seismic data. We believe that our exposition leads to a clear unifying thread between [Formula: see text] and [Formula: see text] beyond-alias interpolation methods and curvelet reconstruction. As in [Formula: see text] and [Formula: see text] interpolation, we stress the necessity of examining seismic data at different scales (frequency bands) to come up with viable and robust interpolation schemes. Synthetic and real data examples are used to illustrate the performance of the proposed curvelet interpolation method.

Case history: Seismic exploration in Egypt’s Eastern Desert

Geophysics ◽  
1992 ◽  
Vol 57 (2) ◽  
pp. 296-305 ◽  
Author(s):  
Norman Mark
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Eastern Desert ◽  
High Energy ◽  
Seismic Exploration ◽  
Depth Of Penetration ◽  
Ground Roll ◽  
New Processing ◽  
Seismic Reflections

Although oil exploration has been performed in the Eastern Desert of Egypt for over a century, seismic reflection techniques have only been in use for less than a fourth of that time. In an effort to improve seismic imaging of geologic targets, many styles of acquisition and processing have been tested, accepted, or discarded. Over the last twenty‐four years, seismic data acquisition has evolved from low‐channel analog to high‐channel digital recordings. The most difficult exploration problems encountered in these efforts have been the low‐frequency and high‐energy ground roll and depth of penetration when imaging the oil producing Pre‐Miocene sandy reservoirs below the highly reflective salt and evaporites. Efforts have been focused on developing seismic processing procedures to enhance the seismic data quality of recently acquired seismic data and developing new acquisition methods to improve seismic data through acquisition and processing. In older acquisition, the new processing has improved the seismic quality (vertical and lateral resolution), but it still retains a low‐frequency character. In the newly acquired seismic data, however, there is improved reflection continuity, depth of penetration, and resolution. We attribute this result to the change from low‐fold (6–24 fold), long receiver and source patterns (50 to 222 m) to high fold (96 fold) short receiver and source group (25 m), and spectral balancing in the processing. The most recent acquisition and processing have greatly improved the quality of the shallow seismic reflections and the deeper reflections that have helped unravel the structural and stratigraphic style of the deeper portions of the basin.

Seismic data interpolation using a fast generalized Fourier transform

Geophysics ◽  
2011 ◽  
Vol 76 (1) ◽  
pp. V1-V10 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Kristopher A. Innanen
Keyword(s):  
Fourier Transform ◽  
Seismic Data ◽  
Interpolation Method ◽  
Temporal Frequency ◽  
Real Data ◽  
Generalized Fourier Transform ◽  
Sampled Data ◽  
Low Frequencies ◽  
Mask Function ◽  
The Ideal

We have found a fast and efficient method for the interpolation of nonstationary seismic data. The method uses the fast generalized Fourier transform (FGFT) to identify the space-wavenumber evolution of nonstationary spatial signals at each temporal frequency. The nonredundant nature of FGFT renders a big computational advantage to this interpolation method. A least-squares fitting scheme is used next to retrieve the optimal FGFT coefficients representative of the ideal interpolated data. For randomly sampled data on a regular grid, we seek a sparse representation of FGFT coefficients to retrieve the missing samples. In addition, to interpolate the regularly sampled seismic data at a given frequency, we use a mask function derived from the FGFT coefficients of the low frequencies. Synthetic and real data examples can be used to examine the performance of the method.

Transform-domain noise synthesis and normal moveout-stack deconvolution approach to ground roll attenuation

Geophysics ◽  
2020 ◽  
Vol 86 (1) ◽  
pp. V15-V22
Author(s):  
Felix Oghenekohwo ◽  
Mauricio D. Sacchi
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Field Data ◽  
Transform Domain ◽  
Coherent Noise ◽  
Least Squares Problem ◽  
Fourier Filtering ◽  
Ground Roll ◽  
Seismic Reflections ◽  
Strong Ground

Ground roll is coherent noise in land seismic data that contaminates seismic reflections. Therefore, it is essential to find efficient ways that remove this noise and still preserve reflections. To this end, we have developed a signal and noise separation framework that uses a hyperbolic moveout assumption on reflections, coupled with the synthesis of coherent ground roll. This framework yields a least-squares problem, which we solve using a sparsity-promoting program that gives coefficients capable of modeling the signal and noise. Subtraction of the predicted noise from the observed data produces data with amplitude-preserved reflections. We develop this technique on synthetic and field data contaminated by weak and strong ground roll noise. Compared to conventional Fourier filtering techniques, our method accurately removes the ground roll while preserving the amplitude of the signal.

Seismic offset balancing

Geophysics ◽  
1994 ◽  
Vol 59 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Christopher P. Ross ◽  
Paul L. Beale
Keyword(s):  
Seismic Data ◽  
Amplitude Variation With Offset ◽  
Reflection Seismic ◽  
Optimal Processing ◽  
Theoretical Predictions ◽  
Back Ground ◽  
Seismic Records ◽  
The Many ◽  
Seismic Reflections ◽  
The Relationship

The ability to successfully predict lithology and fluid content from reflection seismic records using AVO techniques is contingent upon accurate pre‐analysis conditioning of the seismic data. However, all too often, residual amplitude effects remain after the many offset‐dependent processing steps are completed. Residual amplitude effects often represent a significant error when compared to the amplitude variation with offset (AVO) response that we are attempting to quantify. We propose a model‐based, offset‐dependent amplitude balancing method that attempts to correct for these residuals and other errors due to sub‐optimal processing. Seismic offset balancing attempts to quantify the relationship between the offset response of back‐ground seismic reflections and corresponding theoretical predictions for average lithologic interfaces thought to cause these background reflections. It is assumed that any deviation from the theoretical response is a result of residual processing phenomenon and/or suboptimal processing, and a simple offsetdependent scaling function is designed to correct for these differences. This function can then be applied to seismic data over both prospective and nonprospective zones within an area where the theoretical values are appropriate and the seismic characteristics are consistent. A conservative application of the above procedure results in an AVO response over both gas sands and wet sands that is much closer to theoretically expected values. A case history from the Gulf of Mexico Flexure Trend is presented as an example to demonstrate the offset balancing technique.

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