Fast frequency‐wavenumber migration for depth‐dependent velocity

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1483-1491 ◽  
Author(s):  
G. Müller ◽  
P. Temme
Keyword(s):  
Plane Wave ◽  
Constant Velocity ◽  
Layered Medium ◽  
Data Interpretation ◽  
Single Shot ◽  
Subsurface Structures ◽  
Inverse Fourier Transformation ◽  
Interactive Data ◽  
Wave Migration ◽  
Target Depth

Fast frequency‐wavenumber migration for a constant‐velocity medium is generalized for a medium with depth‐dependent velocity and density. As in the constant‐velocity case, the migrated section is obtained with one inverse Fourier transformation of the modified spectrum of the observed wave field. The migrated section is exact in a preselected focusing depth (in the case of common‐midpoint and plane‐wave migration) or in a preselected focusing point (in the case of single‐shot migration) and in the vicinity of this depth or point. Elsewhere the migrated section is distorted. The method is in essence a constant‐velocity migration with the velocity [Formula: see text] at the target depth [Formula: see text] (not with an average velocity), extended by a phase‐shift operation which takes account of the true velocity‐depth function v(z) between z = 0 and [Formula: see text]. If the focusing depth or point is located in a layer with constant velocity and density, the migrated section is exact throughout the layer (for CMP and plane‐wave migration) or along the segment of the ray from shot to focusing point, running through the layer (for single‐shot migration). Examples are given for the successful migration of the plane‐wave response of a horizontally layered medium at vertical and oblique incidence and for the single‐shot response of a horizontally layered structure with embedded reflector elements. The method appears to be particularly useful for the investigation of subsurface structures with restricted dimensions; because of its speed, interactive data interpretation is possible.

A comparison of common‐midpoint, single‐shot, and plane‐wave depth migration

Geophysics ◽  
1984 ◽  
Vol 49 (11) ◽  
pp. 1896-1907 ◽  
Author(s):  
P. Temme
Keyword(s):  
Reflection Coefficient ◽  
Finite Difference ◽  
Plane Wave ◽  
Wave Field ◽  
Single Shot ◽  
Imaging Condition ◽  
Depth Migration ◽  
Finite Aperture ◽  
Wave Migration ◽  
Slant Stacking

A comparison of common‐midpoint (CMP), single‐shot, and plane‐wave migration was made for simple two‐dimensional structures such as a syncline and a horizontal reflector with a laterally variable reflection coefficient by using synthetic seismograms. The seismograms were calculated employing the finite‐difference technique. CMP sections were simulated by 18-fold stacking and plane‐wave sections by slant stacking. By applying a finite‐difference scheme, the synthetic wave field was continued downward. The usual imaging condition of CMP migration was extended in order to carry out migration of single‐shot and plane‐wave sections. The reflection coefficient was reconstructed by comparing the migrated wave field with the incident wave field at the reflector. The results are: (1) all three migration techniques succeeded in reconstructing the reflector position; (2) as a consequence of the finite aperture of the geophone spread, only segments of the reflector could be reconstructed by single‐shot and plane‐wave migration; (3) for single‐shot and plane‐wave migration the reflection coefficient could be obtained; and (4) CMP migration may lead to incorrect conclusions regarding the reflection coefficient.

3D plane-wave migration in tilted coordinates: A field data example

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCA199-WCA209 ◽  
Author(s):  
Guojian Shan ◽  
Robert Clapp ◽  
Biondo Biondi
Keyword(s):  
Plane Wave ◽  
Field Data ◽  
Synthetic Data ◽  
Data Set ◽  
Wave Source ◽  
Surface Data ◽  
The One ◽  
Wave Migration ◽  
Wavefield Extrapolation ◽  
Slant Stacking

We have extended isotropic plane-wave migration in tilted coordinates to 3D anisotropic media and applied it on a Gulf of Mexico data set. Recorded surface data are transformed to plane-wave data by slant-stack processing in inline and crossline directions. The source plane wave and its corresponding slant-stacked data are extrapolated into the subsurface within a tilted coordinate system whose direction depends on the propagation direction of the plane wave. Images are generated by crosscorrelating these two wavefields. The shot sampling is sparse in the crossline direction, and the source generated by slant stacking is not really a plane-wave source but a phase-encoded source. We have discovered that phase-encoded source migration in tilted coordinates can image steep reflectors, using 2D synthetic data set examples. The field data example shows that 3D plane-wave migration in tilted coordinates can image steeply dipping salt flanks and faults, even though the one-way wave-equation operator is used for wavefield extrapolation.

The impact of reciprocity on prestack source plane wave migration

2004 ◽  
Author(s):  
Faqi Liu ◽  
Dan N. Whitmore ◽  
Douglas W. Hanson ◽  
Richard S. Day ◽  
Chuck C. Mosher
Keyword(s):  
Plane Wave ◽  
Source Plane ◽  
Wave Migration ◽  
The Impact

3D plane‐wave migration in tilted coordinates

2007 ◽  
Author(s):  
Guojian Shan ◽  
Robert Clappand ◽  
Biondo Biondi
Keyword(s):  
Plane Wave ◽  
Wave Migration

scholarly journals Deep learning for enhancing multi-source reverse time migration

2022 ◽  
Author(s):  
Yaxing Li ◽  
Xiaofeng Jia ◽  
Xinming Wu ◽  
Zhicheng Geng
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Plane Wave ◽  
Computational Efficiency ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Examples ◽  
Time Migration ◽  
High Resolution Images ◽  
Wave Migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

3D Plane wave migration of streamer data

2001 ◽  
Author(s):  
Bertrand Duquet ◽  
Patrick Lailly ◽  
Andreas Ehinger
Keyword(s):  
Plane Wave ◽  
Streamer Data ◽  
Wave Migration

scholarly journals Delineating groundwater and subsurface structures by using 2D resistivity, gravity and 3D magnetic data interpretation around Cairo–Belbies Desert road, Egypt

2015 ◽  
Vol 4 (1) ◽  
pp. 134-146 ◽  
Author(s):  
Sultan Awad Sultan Araffa ◽  
Ahmed S. Helaly ◽  
Ashraf Khozium ◽  
Amir M.S. Lala ◽  
Shokry A. Soliman ◽  
...  
Keyword(s):  
Data Interpretation ◽  
Magnetic Data ◽  
Subsurface Structures ◽  
2D Resistivity
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