A diffraction imaging method driven by antistationary phase Gaussian beam migration

2016 ◽  
Author(s):  
Peijun Liu
Keyword(s):  
Gaussian Beam ◽  
Imaging Method ◽  
Diffraction Imaging ◽  
Gaussian Beam Migration

scholarly journals Target-oriented Gaussian beam migration using a modified ray tracing scheme

2019 ◽  
Vol 16 (6) ◽  
pp. 1301-1319 ◽  
Author(s):  
Rui Zhang ◽  
Jian-Ping Huang ◽  
Su-Bin Zhuang ◽  
Zhen-Chun Li
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Large Scale ◽  
High Efficiency ◽  
Complex Structure ◽  
Back Propagation ◽  
Imaging Method ◽  
Computation Efficiency ◽  
Simple Implementation ◽  
Gaussian Beam Migration

Abstract For large-scale 3D seismic data, target-oriented reservoir imaging is more attractive than conventional full-volume migration, in terms of computation efficiency. Gaussian beam migration (GBM) is one of the most robust depth imaging method, which not only keeps the advantages of ray methods, such as high efficiency and flexibility, but also allows us to solve caustics and multipathing problems. But conventional Gaussian beam migration requires slant stack for prestack data, and ray tracing from beam center location to subsurface, which is not easy to be directly applied for target-oriented imaging. In this paper, we modify the conventional Gaussian beam migration scheme, by shooting rays from subsurface image points to receivers to implement wavefield back-propagation. This modification helps us to achieve a better subsurface illumination in complex structure and allows simple implementation for target reservoir imaging. Significantly, compared with the wavefield-based GBM, our method does not reconstruct the subsurface snapshots, which has higher efficiency. But the proposed method is not as efficient as the conventional Gaussian beam migration. Synthetic and field data examples demonstrate the validity and the target-oriented imaging capability of our method.

A True-Amplitude Imaging Method Based on Gaussian Beam Migration and Demigration

2020 ◽  
Vol 177 (10) ◽  
pp. 4707-4718
Author(s):  
Shaoyong Liu ◽  
Rushan Wu ◽  
Bo Feng ◽  
Huazhong Wang ◽  
Song Guo
Keyword(s):  
Gaussian Beam ◽  
Imaging Method ◽  
Gaussian Beam Migration

A true-amplitude imaging method based on Gaussian beam migration and demigration

2020 ◽  
Author(s):  
Shaoyong Liu ◽  
Wenting Zhu ◽  
Ru-Shan Wu ◽  
Huazhong Wang ◽  
Song Guo
Keyword(s):  
Gaussian Beam ◽  
Imaging Method ◽  
Gaussian Beam Migration

True-amplitude vector-acoustic imaging: Application of Gaussian beams

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. S43-S54 ◽  
Author(s):  
Nizare El Yadari
Keyword(s):  
Gaussian Beam ◽  
Steep Descent ◽  
Acoustic Imaging ◽  
Imaging Method ◽  
Amplitude Variation With Offset ◽  
Imaging Application ◽  
Gaussian Beam Migration ◽  
The Cost ◽  
The Impact ◽  
Multisensor Data

Acoustic Gaussian beam migration is an attractive imaging method because it is flexible with input geometry, efficient, and accurate in imaging multipath arrivals. However, one of the hurdles that this method must overcome in production processing is its extension to use multimeasurement data, as recently allowed by novel acquisition technologies. This is inevitable when the compensation of the ghost effect is best corrected within a true-amplitude imaging process, a necessity for amplitude-variation-with-offset work. For this purpose, I introduced a novel formalism for vector-acoustic imaging, based on Green’s function theory, which can remove the ghost effect and produce amplitudes on reflectors that are proportional to the reflection coefficients. I established a theoretical framework with Gaussian beam representations of Green’s functions, including the weighted beam-stacking approach that reduced the cost of computation. I extended my formulas to use the steep-descent (i.e., stationary phase) approximation. Then, I explained the impact of this approximation on the illumination and the event continuity and sharpness. I also studied the special case of acoustic imaging corresponding to using single-measurement (i.e., pressure) data. I applied the derived formulations to realistic synthetic multisensor data (North Sea) using a research code of Gaussian beam migration. The numerical examples demonstrated that I can improve the illumination of the final images and obtain wide-bandwidth reflectivity maps.

Least-squares Gaussian beam migration in elastic media

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S329-S340 ◽  
Author(s):  
Yubo Yue ◽  
Paul Sava ◽  
Zhongping Qian ◽  
Jidong Yang ◽  
Zhen Zou
Keyword(s):  
Least Squares ◽  
Gaussian Beam ◽  
Computational Cost ◽  
Synthetic Data ◽  
Preconditioned Conjugate Gradient ◽  
Imaging Method ◽  
Data Sets ◽  
Isotropic Media ◽  
Gaussian Beam Migration ◽  
Multicomponent Data

Gaussian beam migration (GBM) is an effective imaging method that has the ability to image multiple arrivals while preserving the advantages of ray-based methods. We have extended this method to linearized least-squares imaging for elastic waves in isotropic media. We have dynamically transformed the multicomponent data to the principal components of different wave modes using the polarization information available in the beam migration process, and then we use Gaussian beams as wavefield propagator to construct the forward modeling and adjoint migration operators. Based on the constructed operators, we formulate a least-squares migration scheme that is iteratively solved using a preconditioned conjugate gradient method. With this method, we can obtain crosstalk-attenuated multiwave images with better subsurface illumination and higher resolution than those of the conventional elastic Gaussian beam migration. This method also allows us to achieve a good balance between computational cost and imaging accuracy, which are both important requirements for iterative least-squares migrations. Numerical tests on two synthetic data sets demonstrate the validity and effectiveness of our proposed method.

scholarly journals An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4105
Author(s):  
Shaoyong Liu ◽  
Wenting Zhu ◽  
Zhe Yan ◽  
Peng Xu ◽  
Huazhong Wang
Keyword(s):  
Gaussian Beam ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Oil And Gas ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Imaging Method ◽  
Nonlinear Inversion ◽  
Oil And Gas Exploration ◽  
Gaussian Beam Migration

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

Full-field X-ray diffraction microscopy using polymeric compound refractive lenses

2014 ◽  
Vol 47 (6) ◽  
pp. 1882-1888 ◽  
Author(s):  
J. Hilhorst ◽  
F. Marschall ◽  
T. N. Tran Thi ◽  
A. Last ◽  
T. U. Schülli
Keyword(s):  
Imaging Techniques ◽  
Light And Electron Microscopy ◽  
Added Value ◽  
Acquisition Time ◽  
Imaging Method ◽  
X Ray Diffraction ◽  
Polymeric Compound ◽  
Full Field ◽  
X Ray ◽  
Diffraction Imaging

Diffraction imaging is the science of imaging samples under diffraction conditions. Diffraction imaging techniques are well established in visible light and electron microscopy, and have also been widely employed in X-ray science in the form of X-ray topography. Over the past two decades, interest in X-ray diffraction imaging has taken flight and resulted in a wide variety of methods. This article discusses a new full-field imaging method, which uses polymer compound refractive lenses as a microscope objective to capture a diffracted X-ray beam coming from a large illuminated area on a sample. This produces an image of the diffracting parts of the sample on a camera. It is shown that this technique has added value in the field, owing to its high imaging speed, while being competitive in resolution and level of detail of obtained information. Using a model sample, it is shown that lattice tilts and strain in single crystals can be resolved simultaneously down to 10−3° and Δa/a= 10−5, respectively, with submicrometre resolution over an area of 100 × 100 µm and a total image acquisition time of less than 60 s.

Gaussian beam migration

Geophysics ◽  
1990 ◽  
Vol 55 (11) ◽  
pp. 1416-1428 ◽  
Author(s):  
N. Ross Hill
Keyword(s):  
Gaussian Beam ◽  
Wave Field ◽  
Seismic Source ◽  
Velocity Model ◽  
Downward Continuation ◽  
Gaussian Beams ◽  
Seismic Velocities ◽  
Migration Method ◽  
Gaussian Beam Migration ◽  
Lateral Variations

Just as synthetic seismic data can be created by expressing the wave field radiating from a seismic source as a set of Gaussian beams, recorded data can be downward continued by expressing the recorded wave field as a set of Gaussian beams emerging at the earth’s surface. In both cases, the Gaussian beam description of the seismic‐wave propagation can be advantageous when there are lateral variations in the seismic velocities. Gaussian‐beam downward continuation enables wave‐equation calculation of seismic propagation, while it retains the interpretive raypath description of this propagation. This paper describes a zero‐offset depth migration method that employs Gaussian beam downward continuation of the recorded wave field. The Gaussian‐beam migration method has advantages for imaging complex structures. Like finite‐difference migration, it is especially compatible with lateral variations in velocity, but Gaussian beam migration can image steeply dipping reflectors and will not produce unwanted reflections from structure in the velocity model. Unlike other raypath methods, Gaussian beam migration has guaranteed regular behavior at caustics and shadows. In addition, the method determines the beam spacing that ensures efficient, accurate calculations. The images produced by Gaussian beam migration are usually stable with respect to changes in beam parameters.

Seismic diffraction separation in the near-surface: Detection of high contrast voids in unconsolidated soils

Geophysics ◽  
2021 ◽  
pp. 1-72
Author(s):  
Parsa Bakhtiari Rad ◽  
Craig J. Hickey
Keyword(s):  
Soft Soil ◽  
Processing Parameters ◽  
Imaging Method ◽  
Data Sets ◽  
High Contrast ◽  
Near Surface ◽  
Time Migration ◽  
Diffraction Imaging ◽  
Dip Angle ◽  
Scattering Time

Seismic diffractions carry the signature of near-surface high-contrast anomalies and need to be extracted from the data to complement the reflection processing and other geophysical techniques. Since diffractions are often masked by reflections, surface waves and noise, a careful diffraction separation is required as a first step for diffraction imaging. A multiparameter time-imaging method is employed to separate near-surface diffractions. The implemented scheme makes use of the wavefront attributes that are reliable fully data-derived processing parameters. To mitigate the effect of strong noise and wavefield interference in near-surface data, the proposed workflow incorporates two wavefront-based parameters, dip angle and coherence, as additional constraints. The output of the diffraction separation is a time trace-based stacked section that provides the basis for further analysis and applications such as time migration. To evaluate the performance of the proposed wavefront-based workflow, it is applied to two challenging field data sets that were collected over small culverts in very near-surface soft soil environments. The results of the proposed constrained workflow and the existing unconstrained approach are presented and compared. The proposed workflow demonstrates superiority over the existing method by attenuating more reflection and noise, leading to improved diffraction separation. The abundance of unmasked diffractions reveal that the very near-surface is highly scattering. Time migration is carried out to enhance the anomaly detection by focusing of the isolated diffractions. Although strong diffractivity is observed at the approximate location of the targets, there are other diffracting zones observed in the final sections that might bring uncertainties for interpretation.

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