Velocity-estimation improvements and migration/demigration using the common-reflection surface with continuing deconvolution in the time domain

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S229-S238 ◽  
Author(s):  
Martina Glöckner ◽  
Sergius Dell ◽  
Benjamin Schwarz ◽  
Claudia Vanelle ◽  
Dirk Gajewski
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Initial Time ◽  
Model Errors ◽  
Imaging Methods ◽  
Time Migration ◽  
Common Reflection Surface ◽  
The Common ◽  
And Migration

To obtain an image of the earth’s subsurface, time-imaging methods can be applied because they are reasonably fast, are less sensitive to velocity model errors than depth-imaging methods, and are usually easy to parallelize. A powerful tool for time imaging consists of a series of prestack time migrations and demigrations. We have applied multiparameter stacking techniques to obtain an initial time-migration velocity model. The velocity model building proposed here is based on the kinematic wavefield attributes of the common-reflection surface (CRS) method. A subsequent refinement of the velocities uses a coherence filter that is based on a predetermined threshold, followed by an interpolation and smoothing. Then, we perform a migration deconvolution to obtain the final time-migrated image. The migration deconvolution consists of one iteration of least-squares migration with an estimated Hessian. We estimate the Hessian by nonstationary matching filters, i.e., in a data-driven fashion. The model building uses the framework of the CRS, and the migration deconvolution is fully automated. Therefore, minimal user interaction is required to carry out the velocity model refinement and the image update. We apply the velocity refinement and migration deconvolution approaches to complex synthetic and field data.

Determination of wavefront attributes by differential evolution in the presence of conflicting dips

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V229-V239 ◽  
Author(s):  
Jan Walda ◽  
Dirk Gajewski
Keyword(s):  
Differential Evolution ◽  
Model Building ◽  
Optimization Technique ◽  
Velocity Model ◽  
Search Space ◽  
Global Maximum ◽  
Local Maxima ◽  
Parameter Search ◽  
Common Reflection Surface ◽  
And Migration

The common-reflection surface (CRS) method represents a multidimensional stacking approach; i.e., the stacking surface is determined in the midpoint and offset directions. In the 2D case, three attributes span the stacking surface, thus requiring a three-parameter search contrary to a one-parameter search in the classic common-midpoint stack. However, CRS wavefront attributes use data redundancy in the midpoint direction as well, which makes them very useful in several seismic applications, e.g., data preconditioning, velocity model building, and migration. Contrary to previous works, we simultaneously estimate CRS attributes using differential evolution in subcubes of the 3D search space. Differential evolution is a global optimization technique that performs particularly well when the objective function is unknown. Because we apply DE for each subcube, we could find local maxima, additionally to the global maximum. Therefore, conflicting dips are recognized and can be used for the stack and subsequent CRS attribute-based processing, which has been an issue in the past. Our land data results from the Donbas Foldbelt in southeast Ukraine demonstrate that our method reduces coherent steep dipping noise and reveal more subsurface structures. Application of the CRS attributes for prestack data enhancement shows that velocity analysis can be carried out more reliably.

Dirty salt velocity inversion: The road to a clearer subsalt image

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB169-WB174 ◽  
Author(s):  
Shuo Ji ◽  
Tony Huang ◽  
Kang Fu ◽  
Zhengxue Li
Keyword(s):  
Model Building ◽  
Synthetic Data ◽  
Velocity Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Velocity Inversion ◽  
Time Migration ◽  
And Migration ◽  
Subsalt Imaging

For deep-water Gulf of Mexico, accurate salt geometry is critical to subsalt imaging. This requires the definition of both external and internal salt geometries. In recent years, external salt geometry (i.e., boundaries between allochthonous salt and background sediment) has improved a great deal due to advances in acquisition, velocity model building, and migration algorithms. But when it comes to defining internal salt geometry (i.e., intrasalt inclusions or dirty salt), no efficient method has yet been developed. In common industry practices, intrasalt inclusions (and thus their velocity anomalies) are generally ignored during the model building stages. However, as external salt geometries reach higher levels of accuracy, it becomes more important to consider the once-ignored effects of dirty salt. We have developed a reflectivity-based approach for dirty salt velocity inversion. This method takes true-amplitude reverse time migration stack volumes as input, then estimates the dirty salt velocity based on reflectivity under a 1D assumption. Results from a 2D synthetic data set and a real 3D Wide Azimuth data set demonstrated that the reflectivity inversion scheme significantly improves the subsalt image for certain areas. In general, we believe that this method produces a better salt model than the traditional clean salt velocity approach.

scholarly journals Common-reflection-surface-based prestack diffraction separation and imaging

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. S47-S55 ◽  
Author(s):  
Parsa Bakhtiari Rad ◽  
Benjamin Schwarz ◽  
Dirk Gajewski ◽  
Claudia Vanelle
Keyword(s):  
Model Building ◽  
Synthetic Data ◽  
Velocity Model ◽  
Small Scale ◽  
Time Migration ◽  
Velocity Model Building ◽  
Common Reflection Surface ◽  
Diffraction Imaging ◽  
Zero Offset ◽  
Velocity Spectra

Diffraction imaging can lead to high-resolution characterization of small-scale subsurface structures. A key step of diffraction imaging and tomography is diffraction separation and enhancement, especially in the full prestack data volume. We have considered point diffractors and developed a robust and fully data-driven workflow for prestack diffraction separation based on wavefront attributes, which are determined using the common-reflection-surface (CRS) method. In the first of two steps, we apply a zero-offset-based extrapolation operator for prestack diffraction separation, which combines the robustness and stability of the zero-offset CRS processing with enhanced resolution and improved illumination of the finite-offset CRS processing. In the second step, when the finite-offset diffracted events are separated, we apply a diffraction-based time migration velocity model building that provides high-quality diffraction velocity spectra. Applications of the new workflow to 2D/3D complex synthetic data confirm the superiority of prestack diffraction separation over the poststack method as well as the high potential of diffractions for improved time imaging.

Use of RTM full 3D subsurface angle gathers for subsalt velocity update and image optimization: Case study at Shenzi field

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB27-WB39 ◽  
Author(s):  
Zheng-Zheng Zhou ◽  
Michael Howard ◽  
Cheryl Mifflin
Keyword(s):  
Model Building ◽  
Transverse Isotropy ◽  
Velocity Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Edge Diffraction ◽  
Time Migration ◽  
Image Optimization ◽  
Free Generation

Various reverse time migration (RTM) angle gather generation techniques have been developed to address poor subsalt data quality and multiarrival induced problems in gathers from Kirchhoff migration. But these techniques introduce new problems, such as inaccuracies in 2D subsurface angle gathers and edge diffraction artifacts in 3D subsurface angle gathers. The unique rich-azimuth data set acquired over the Shenzi field in the Gulf of Mexico enabled the generally artifact-free generation of 3D subsurface angle gathers. Using this data set, we carried out suprasalt tomography and salt model building steps and then produced 3D angle gathers to update the subsalt velocity. We used tilted transverse isotropy RTM with extended image condition to generate full 3D subsurface offset domain common image gathers, which were subsequently converted to 3D angle gathers. The angle gathers were substacked along the subsurface azimuth axis into azimuth sectors. Residual moveout analysis was carried out, and ray-based tomography was used to update velocities. The updated velocity model resulted in improved imaging of the subsalt section. We also applied residual moveout and selective stacking to 3D angle gathers from the final migration to produce an optimized stack image.

Time-migration velocity analysis by image-wave propagation of common-image gathers

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE161-VE171 ◽  
Author(s):  
J. Schleicher ◽  
J. C. Costa ◽  
A. Novais
Keyword(s):  
Wave Propagation ◽  
Seismic Event ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Reference Velocity ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
Velocity Image ◽  
The Common

Image-wave propagation or velocity continuation describes the variation of the migrated position of a seismic event as a function of migration velocity. Image-wave propagation in the common-image gather (CIG) domain can be combined with residual-moveout analysis for iterative migration velocity analysis (MVA). Velocity continuation of CIGs leads to a detection of those velocities in which events flatten. Although image-wave continuation is based on the assumption of a constant migration velocity, the procedure can be applied in inhomogeneous media. For this purpose, CIGs obtained by migration with an inhomogeneous macrovelocity model are continued starting from a constant reference velocity. The interpretation of continued CIGs, as if they were obtained from residual migrations, leads to a correction formula that translates residual flattening velocities into absolute time-migration velocities. In this way, the migration velocity model can be improved iteratively until a satisfactory result is reached. With a numerical example, we found that MVA with iterative image continuation applied exclusively to selected CIGs can construct a reasonable migration velocity model from scratch, without the need to build an initial model from a previous conventional normal-moveout/dip-moveout velocity analysis.

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

Surface-offset gathers from elastic reverse time migration and velocity analysis

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. S47-S64
Author(s):  
Yang Zhao ◽  
Tao Liu ◽  
Xueyi Jia ◽  
Hongwei Liu ◽  
Zhiguang Xue ◽  
...  
Keyword(s):  
Velocity Ratio ◽  
Computational Cost ◽  
Cost Effective ◽  
Velocity Estimation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
S Wave ◽  
Time Migration ◽  
The Common ◽  
Wave Decomposition

Angle-domain common-image gathers (ADCIGs) from elastic reverse time migration (ERTM) are valuable tools for seismic elastic velocity estimation. Traditional ADCIGs are based on the concept of common-offset domains, but common-shot domain implementations are often favored for computational cost considerations. Surface-offset gathers (SOGs) built from common-offset migration may serve as an alternative to the common-shot ADCIGs. We have developed a theoretical kinematic framework between these two domains, and we determined that the common SOG gives an alternative measurement of kinematic correctness in the presence of incorrect velocity. Specifically, we exploit analytical expressions for the image misposition between these two domains, with respect to the traveltime perturbation caused by velocity errors. Four formulations of the PP and PS residual moveout functions are derived and provide insightful information of the velocity error, angle, and PS velocity ratio contained in ERTM gathers. The analytical solutions are validated with homogeneous examples with a series of varied parameters. We found that the SOGs may perform in the way of simplicity and linearity as an alternative to the common-shot migration. To make a full comparison with ADCIGs, we have developed a cost-effective workflow of ERTM SOGs. A fast vector P- and S-wave decomposition can be obtained via spatial gradients at selected time steps. A selected ERTM imaging condition is then modified in which the migration is done by offset groups between each source and receiver pair for each P- and S-wave decomposition. Two synthetic (marine and land) examples are used to demonstrate the feasibility of our methods.

Redatuming through a salt canopy and target-oriented salt-flank imaging

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. S63-S71 ◽  
Author(s):  
Rongrong Lu ◽  
Mark Willis ◽  
Xander Campman ◽  
Jonathan Ajo-Franklin ◽  
M. Nafi Toksöz
Keyword(s):  
Velocity Gradient ◽  
Model Building ◽  
Velocity Structure ◽  
Velocity Model ◽  
Seismic Profile ◽  
Velocity Estimation ◽  
Reverse Time ◽  
Estimation Model ◽  
Depth Migration ◽  
Prestack Migration

We describe a new shortcut strategy for imaging the sediments and salt edge around a salt flank through an overburden salt canopy. We tested its performance and capabilities on 2D synthetic acoustic seismic data from a Gulf of Mexico style model. We first redatumed surface shots, using seismic interferometry, from a walkaway vertical seismic profile survey as if the source and receiver pairs had been located in the borehole at the positions of the receivers. This process creates effective downhole shot gathers by completely moving surface shots through the salt canopy, without any knowledge of overburden velocity structure. After redatuming, we can apply multiple passes of prestack migration from the reference datum of the bore-hole. In our example, first-pass migration, using only a simple vertical velocity gradient model, reveals the outline of the salt edge. A second pass of reverse-time, prestack depth migration using full two-way wave equation was performed with an updated velocity model that consisted of the velocity gradient and salt dome. The second-pass migration brings out dipping sediments abutting the salt flank because these reflectors were illuminated by energy that bounced off the salt flank, forming prismatic reflections. In this target-oriented strategy, the computationally fast redatuming process eliminates the need for the traditional complex process of velocity estimation, model building, and iterative depth migration to remove effects of the salt canopy and surrounding overburden. This might allow this strategy to be used in the field in near real time.

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