Nonstretching NMO correction of prestack time-migrated gathers using a matching-pursuit algorithm

Wide-azimuth, long-offset surveys are becoming increasingly common in unconventional exploration plays where one of the key routine processes is maintaining data fidelity at far offsets. The conventional NMO correction that processes the data sample-by-sample results in the well-known decrease of frequency content and amplitude distortion through stretch, which lowers the seismic resolution and hinders [Formula: see text] and amplitude variation with offset and azimuth (AVAz) analysis of the long-offset signal. To mitigate the stretch typically associated with large offsets, we use a matching-pursuit-based normal moveout correction (MPNMO) to reduce NMO-stretch effects in long-offset data. MPNMO corrects the data wavelet-by-wavelet rather than sample-by-sample, thereby avoiding stretch. We apply our technique (1) to a set of synthetic gathers and (2) as part of a residual velocity analysis workflow to a prestack time-migrated data volume acquired over the Northern Chicontepec Basin, Mexico. Test results show that MPNMO can produce relatively nonstretched events and generate higher temporal resolution prestack gathers.

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The Shuey-Radon transform

Geophysics ◽

10.1190/geo2018-0376.1 ◽

2019 ◽

Vol 84 (3) ◽

pp. V197-V206 ◽

Cited By ~ 1

Author(s):

Ali Gholami ◽

Milad Farshad

Keyword(s):

Radon Transform ◽

Seismic Data ◽

Matching Pursuit ◽

Real Data ◽

Transform Domain ◽

Amplitude Variation ◽

Amplitude Variation With Offset ◽

Linear System Of Equations ◽

Matching Pursuit Algorithm ◽

Zero Offset

The traditional hyperbolic Radon transform (RT) decomposes seismic data into a sum of constant amplitude basis functions. This limits the performance of the transform when dealing with real data in which the reflection amplitudes include the amplitude variation with offset (AVO) variations. We adopted the Shuey-Radon transform as a combination of the RT and Shuey’s approximation of reflectivity to accurately model reflections including AVO effects. The new transform splits the seismic gather into three Radon panels: The first models the reflections at zero offset, and the other two panels add capability to model the AVO gradient and curvature. There are two main advantages of the Shuey-Radon transform over similar algorithms, which are based on a polynomial expansion of the AVO response. (1) It is able to model reflections more accurately. This leads to more focused coefficients in the transform domain and hence provides more accurate processing results. (2) Unlike polynomial-based approaches, the coefficients of the Shuey-Radon transform are directly connected to the classic AVO parameters (intercept, gradient, and curvature). Therefore, the resulting coefficients can further be used for interpretation purposes. The solution of the new transform is defined via an underdetermined linear system of equations. It is formulated as a sparsity-promoting optimization, and it is solved efficiently using an orthogonal matching pursuit algorithm. Applications to different numerical experiments indicate that the Shuey-Radon transform outperforms the polynomial and conventional RTs.

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Horizon-based semiautomated nonhyperbolic velocity analysis

Geophysics ◽

10.1190/geo2014-0112.1 ◽

2014 ◽

Vol 79 (6) ◽

pp. U15-U23 ◽

Cited By ~ 12

Author(s):

Bo Zhang ◽

Tao Zhao ◽

Jie Qi ◽

Kurt J. Marfurt

Keyword(s):

Seismic Exploration ◽

Velocity Analysis ◽

Small Aperture ◽

Interval Model ◽

Fort Worth Basin ◽

Automated Algorithm ◽

Long Offset ◽

Noticeable Improvement ◽

Data Volume ◽

Nmo Stretch

With higher capacity recording systems, long-offset surveys are becoming common in seismic exploration plays. Long offsets provide leverage against multiples, have greater sensitivity to anisotropy, and are key to accurate inversion for shear impedance and density. There are two main issues associated with preserving the data fidelity contained in the large offsets: (1) nonhyperbolic velocity analysis and (2) mitigating the migration/NMO stretch. Current nonhyperbolic velocity analysis workflows first estimate moveout velocity [Formula: see text] based on the offset-limited gathers, then pick an effective anellipticity [Formula: see text] using the full-offset gathers. Unfortunately, estimating [Formula: see text] at small aperture may be inaccurate, with picking errors in [Formula: see text] introducing errors in the subsequent analysis of effective anellipticity. We have developed an automated algorithm to simultaneously estimate the nonhyperbolic parameters. Instead of directly seeking an effective stacking model, the algorithm finds an interval model that gives the most powerful stack. The searching procedure for the best interval model was conducted using a direct, global optimization algorithm called differential evolutionary. Next, we applied an antistretch workflow to minimize stretch at a far offset after obtaining the optimal effective model. The automated velocity analysis and antistretch workflow were tested on the data volume acquired over the Fort Worth Basin, USA. The results provided noticeable improvement on the prestack gathers and on the stacked data volume.

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A modified nonstretching NMO correction using matching-pursuit algorithm

10.1190/segam2014-1034.1 ◽

2014 ◽

Author(s):

Xiaohui Yang ◽

Siyuan Cao ◽

Qiong Liu ◽

Yuzhou Wang ◽

Dian Yuan

Keyword(s):

Matching Pursuit ◽

Nmo Correction ◽

Matching Pursuit Algorithm

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Nonstretch normal moveout through iterative partial correction and deconvolution

Geophysics ◽

10.1190/geo2013-0392.1 ◽

2014 ◽

Vol 79 (4) ◽

pp. V131-V141 ◽

Cited By ~ 8

Author(s):

Ettore Biondi ◽

Eusebio Stucchi ◽

Alfredo Mazzotti

Keyword(s):

Seismic Data ◽

Singular Value ◽

Long Offset ◽

Nmo Correction ◽

Seismic Data Acquisition ◽

Marine Data ◽

Value Decomposition ◽

Nmo Stretch ◽

Space Variant ◽

Partial Correction

Source to receiver distances used in seismic data acquisition have been steadily increasing and it is now common to work with data acquired with more than 10 km of offset. Subbasalt exploration and seismic undershooting are just two applications in which long-offset reflections are sought. However, such reflections are often subjected to muting to suppress normal moveout (NMO) stretch artifacts, thus causing a loss of valuable information. To retrieve these portions of the recorded wavefield, we developed a nonstretch NMO correction based on wavelet estimation and on an iterative procedure of partial NMO correction and deconvolution. We evaluated this methodology using fourth-order traveltime curve approximations to increase the offset of usable reflections, but it can be adapted to traveltime curves of any order. Time- and space-variant wavelets, estimated by means of singular value decomposition along the sought traveltimes, were used to build the desired output for the deconvolution that aims at retrieving the original shape of the partially stretched wavelets. We tested our method on a synthetic gather presenting time and offset varying wavelets, on a real-marine line simulating an undershooting pattern and on true undershooting land-marine data. These examples demonstrated that our new algorithm effectively limits the stretching associated with the NMO correction and enables the recovery of those portions of the stacked sections that are typically lost from muting in the standard NMO correction.

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A method to compensate for migration stretch to improve the resolution of amplitude variation with offset, S-impedance (ZS), and density (ρ)

Interpretation ◽

10.1190/int-2019-0226.1 ◽

2020 ◽

Vol 8 (4) ◽

pp. T687-T699

Author(s):

Swetal Patel ◽

Francis Oyebanji ◽

Kurt J. Marfurt

Keyword(s):

Matching Pursuit ◽

Azimuthal Anisotropy ◽

Amplitude Variation ◽

Amplitude Variation With Offset ◽

Prestack Migration ◽

Fort Worth Basin ◽

Seismic Wavelet ◽

Compensation Factor ◽

Nmo Correction ◽

And Migration

Because of their improved leverage against ground roll and multiples, as well as the ability to estimate azimuthal anisotropy, wide-azimuth 3D seismic surveys routinely now are acquired over most resource plays. For a relatively shallow target, most of these surveys can be considered to be long offset as well, containing incident angles up to 45°. Unfortunately, effective use of the far-offset data often is compromised by noise and normal moveout (NMO) (or, more accurately, prestack migration) stretch. The conventional NMO correction is well-known to decrease the frequency content and distort the seismic wavelet at far offsets, sometimes giving rise to tuning effects. Most quantitative interpreters work with prestack migrated gathers rather than unmigrated NMO-corrected gathers. However, prestack migration of flat reflectors suffers from the same limitation called migration stretch. Migration stretch leads to lower S-impedance ([Formula: see text]) and density ([Formula: see text]) resolution estimated from inversion, misclassification of amplitude variation with offset (AVO) types, and infidelity in amplitude variation with azimuth (AVAZ) inversion results. We have developed a matching pursuit algorithm commonly used in spectral decomposition to correct the migration stretch by scaling the stretched wavelets using a wavelet compensation factor. The method is based on hyperbolic moveout approximation. The corrected gathers show increased resolution and higher fidelity amplitudes at the far offsets leading to improvement in AVO classification. Correction for migration stretch rather than conventional “stretch-mute” corrections provides three advantages: (1) preservation of far angles required for accurate [Formula: see text] inversion, (2) improvement in the vertical resolution of [Formula: see text] and [Formula: see text] volumes, and (3) preservation of far angles that provide greater leverage against multiples. We apply our workflow to data acquired in the Fort Worth Basin and retain incident angles up to 42° at the Barnett Shale target. Comparing [Formula: see text], [Formula: see text], and [Formula: see text] of the original gather and migration stretch-compensated data, we find an insignificant improvement in [Formula: see text], but a moderate to significant improvement in resolution of [Formula: see text] and [Formula: see text]. The method is valid for reservoirs that exhibit a dip of no more than 2°. Consistent improvement is observed in resolving thick beds, but the method might introduce amplitude anomalies at far offsets for tuning beds.

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Sparsity adaptive matching pursuit algorithm based on adaptive threshold for OFDM sparse channel estimation

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.01508 ◽

2013 ◽

Vol 33 (6) ◽

pp. 1508-1510

Author(s):

Shan JIANG ◽

Hongbing QIU ◽

Xu HAN

Keyword(s):

Channel Estimation ◽

Matching Pursuit ◽

Adaptive Threshold ◽

Sparse Channel Estimation ◽

Matching Pursuit Algorithm ◽

Sparse Channel

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A Broadband Spectrum Sensing Algorithm in TDCS Based on ICoSaMP Reconstruction

MATEC Web of Conferences ◽

10.1051/matecconf/201817303073 ◽

2018 ◽

Vol 173 ◽

pp. 03073

Author(s):

Liu Yang ◽

Ren Qinghua ◽

Xu Bingzheng ◽

Li Xiazhao

Keyword(s):

Time Use ◽

Matching Pursuit ◽

Reconstruction Algorithm ◽

Transform Domain ◽

Low Snr ◽

System Noise ◽

Broadband Spectrum ◽

Matching Pursuit Algorithm ◽

The Mean ◽

Occupancy Rates

In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.

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