3-D Modeling Project: 3rd report

Aminzadeh, F.; Burkhard, N.R.; Kunz, T.J.; Nicoletis, L.; Rocca, F.

doi:10.1190/1.1437102

Cited by 79 publications

(25 citation statements)

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“…We exemplify the method with illumination, including DI and ADR, for downgoing waves in the 2D SEG/EAGE salt model (Aminzadeh et al, 1994;Aminzadeh et al, 1995), turning waves in a v(z) model, and reflected waves in a two-layer model.…”

Section: Local-angle Domain Illumination Resultsmentioning

confidence: 99%

“…Then the new implementation is described. We exemplify the method with the GDF and the LEF decomposition in 2D SEG/EAGE salt model (Aminzadeh et al, 1994;Aminzadeh et al, 1995) and compare the results with those from the original method. We also discuss the possible factors causing the difference in the results by the GDF and the LEF decomposition.…”

Section: Fast Acquisition Aperture Correction By Beamlet Decompositionmentioning

confidence: 99%

“…We exemplify the new implementation of LAD acquisition aperture correction with 2D SEG/EAGE salt model (X Figure 8.2) data set (Aminzadeh et al, 1994;Aminzadeh et al, 1995). The whole acquisition system of this model consists of 325 shots with 176 left-side trailing receivers for each shot and the shot and receiver intervals are 160 and 80 feet respectively.…”

Section: Examplesmentioning

confidence: 99%

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High Resolution/High Fidelity Seismic Imaging and Parameter Estimation for Geological Structure and Material Characterization

Wu¹,

Xie²

2008

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Section: Local-angle Domain Illumination Resultsmentioning

confidence: 99%

Section: Fast Acquisition Aperture Correction By Beamlet Decompositionmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

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High Resolution/High Fidelity Seismic Imaging and Parameter Estimation for Geological Structure and Material Characterization

Wu¹,

Xie²

2008

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“…Noisy synthetic data (Figure 1a) are generated by first convolving a 30-Hz Ricker wavelet with the reflectivity derived from the SEG/ EAGE overthrust model (Aminzadeh et al, 1995), then randomly evacuating 200 traces, and finally adding 5% random noise (i.e., the ratio of noise energy to signal energy is 5%). In the data set with a time interval of 2 ms and a space interval of 25 m, 200 null traces are set to zero for showing the distribution of these traces, whereas the other 600 traces are considered as the observed data to participate in the inversion.…”

Section: Synthetic Data Examplementioning

confidence: 99%

Simultaneous multitrace impedance inversion with transform-domain sparsity promotion

et al. 2015

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The impedance inversion technique plays a crucial role in seismic reservoir properties prediction. However, most existing impedance inversion methods often suffer from spatial discontinuities and instability because each vertical profile is processed independently in the inversion. We tested a transform-domain sparsity promotion simultaneous multitrace impedance inversion method to address this issue. The approach was implemented through minimizing a data misfit term and a transform-domain sparsity constraint term that incorporates the (2D or 3D) structural information into the inversion processing. A 2D synthetic data example was applied to mainly explain the roles of the transform-domain sparsity constraint. We determined that the transform-domain sparsity constraint can help in stabilizing the inversion, reducing the influence of high-wavenumber noise on the inverted result, and exploring spatial continuities of structures. Furthermore, a 3D field data example was used to examine the effectiveness of the proposed method for dealing with the real data and to reveal the difference between the results from the 3D simultaneous inversion and the section-by-section inversion. We found that the inverted results roughly matched a lowpass-filtered version of impedance curves derived from well log data. Also, it has been demonstrated that the 3D simultaneous inversion technique provided a better estimation than the section-by-section inversion technique in terms of guaranteeing more spatial continuities of geologic features in the impedance model.

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“…1994a: Aminzadeh et al. 1994b: Aminzadeh et al. 1995, and a follow-up project funded as part of the Advanced Computational Technology Initiative (ACTI) (House et ai.. 1996) have sought t o investigate problems with imaging beneath complex salt structures using numerical modeling and more recently.…”

Section: Sumriarymentioning

confidence: 99%

Numerical and measured data from the 3D salt canopy physical modeling project

Bradley

Fehler

Pearson

et al. 1997

SEG Technical Program Expanded Abstracts 1997

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Oil Co. SUMRIARYThe evolution of salt structures in the Gulf of Mexico have been shown to provide a mechanism for the trapping of significant hydrocarbon reserves. Most of these structures have complex geometries relative to the surrounding sedimentary layers. This aspect in addition to high velocities within the salt tend to scatter and defocus seismic energy and make imaging of subsalt lithology extremely difficult. To date. no direct comparison of the numerical and physical aspects of these models has been attempted. We present the results of fonvard modeling a numerical realization ofthe 3D salt canopy physical model with the French Petroleum Institute (IFP) acoustic finite difference algorithm used in the numerical subsalt tests.We compare the results h m the physical salt canopy model. the acoustic modeling of the physicalhumerical model and the original numerical SEGI'EAEG Salt Model. We will be testing the sensitivity of migration to the presence of converted shear waves and acquisition geometry.

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3-D Modeling Project: 3rd report

Cited by 79 publications

References 0 publications

High Resolution/High Fidelity Seismic Imaging and Parameter Estimation for Geological Structure and Material Characterization

High Resolution/High Fidelity Seismic Imaging and Parameter Estimation for Geological Structure and Material Characterization

Simultaneous multitrace impedance inversion with transform-domain sparsity promotion

Numerical and measured data from the 3D salt canopy physical modeling project

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