Migration velocity analysis based on focusing diffractions generated using the CRS wavefront attributes: Application to real land data

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
German Garabito ◽  
José Silas dos Santos Silva ◽  
Williams Lima
Keyword(s):  
Time Domain ◽  
Real Data ◽  
Normal Incidence ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Velocity Models ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
The Time Domain

In land seismic data processing, the prestack time migration (PSTM) image remains the standard imaging output, but a reliable migrated image of the subsurface depends on the accuracy of the migration velocity model. We have adopted two new algorithms for time-domain migration velocity analysis based on wavefield attributes of the common-reflection-surface (CRS) stack method. These attributes, extracted from multicoverage data, were successfully applied to build the velocity model in the depth domain through tomographic inversion of the normal-incidence-point (NIP) wave. However, there is no practical and reliable method for determining an accurate and geologically consistent time-migration velocity model from these CRS attributes. We introduce an interactive method to determine the migration velocity model in the time domain based on the application of NIP wave attributes and the CRS stacking operator for diffractions, to generate synthetic diffractions on the reflection events of the zero-offset (ZO) CRS stacked section. In the ZO data with diffractions, the poststack time migration (post-STM) is applied with a set of constant velocities, and the migration velocities are then selected through a focusing analysis of the simulated diffractions. We also introduce an algorithm to automatically calculate the migration velocity model from the CRS attributes picked for the main reflection events in the ZO data. We determine the precision of our diffraction focusing velocity analysis and the automatic velocity calculation algorithms using two synthetic models. We also applied them to real 2D land data with low quality and low fold to estimate the time-domain migration velocity model. The velocity models obtained through our methods were validated by applying them in the Kirchhoff PSTM of real data, in which the velocity model from the diffraction focusing analysis provided significant improvements in the quality of the migrated image compared to the legacy image and to the migrated image obtained using the automatically calculated velocity model.

Migration velocity analysis from locally coherent events in 2‐D laterally heterogeneous media, Part I: Theoretical aspects

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1202-1212 ◽  
Author(s):  
Hervé Chauris ◽  
Mark S. Noble ◽  
Gilles Lambaré ◽  
Pascal Podvin
Keyword(s):  
Cost Function ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Migration Velocity Analysis ◽  
Paraxial Ray ◽  
The Cost

We present a new method based on migration velocity analysis (MVA) to estimate 2‐D velocity models from seismic reflection data with no assumption on reflector geometry or the background velocity field. Classical approaches using picking on common image gathers (CIGs) must consider continuous events over the whole panel. This interpretive step may be difficult—particularly for applications on real data sets. We propose to overcome the limiting factor by considering locally coherent events. A locally coherent event can be defined whenever the imaged reflectivity locally shows lateral coherency at some location in the image cube. In the prestack depth‐migrated volume obtained for an a priori velocity model, locally coherent events are picked automatically, without interpretation, and are characterized by their positions and slopes (tangent to the event). Even a single locally coherent event has information on the unknown velocity model, carried by the value of the slope measured in the CIG. The velocity is estimated by minimizing these slopes. We first introduce the cost function and explain its physical meaning. The theoretical developments lead to two equivalent expressions of the cost function: one formulated in the depth‐migrated domain on locally coherent events in CIGs and the other in the time domain. We thus establish direct links between different methods devoted to velocity estimation: migration velocity analysis using locally coherent events and slope tomography. We finally explain how to compute the gradient of the cost function using paraxial ray tracing to update the velocity model. Our method provides smooth, inverted velocity models consistent with Kirchhoff‐type migration schemes and requires neither the introduction of interfaces nor the interpretation of continuous events. As for most automatic velocity analysis methods, careful preprocessing must be applied to remove coherent noise such as multiples.

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.

Migration velocity analysis from locally coherent events in 2‐D laterally heterogeneous media, Part II: Applications on synthetic and real data

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1213-1224 ◽  
Author(s):  
Hervé Chauris ◽  
Mark S. Noble ◽  
Gilles Lambaré ◽  
Pascal Podvin
Keyword(s):  
Heterogeneous Media ◽  
Real Data ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Efficient Computation ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Migration Velocity Analysis ◽  
Paraxial Ray

We demonstrate a method for estimating 2‐D velocity models from synthetic and real seismic reflection data in the framework of migration velocity analysis (MVA). No assumption is required on the reflector geometry or on the unknown background velocity field, provided that the data only contain primary reflections/diffractions. In the prestack depth‐migrated volume, locations where the reflectivity exhibits local coherency are automatically picked without interpretation in two panels: common image gathers (CIGs) and common offset gathers (COGs). They are characterized by both their positions and two slopes. The velocity is estimated by minimizing all slopes picked in the CIGs. We test the applicability of the method on a real data set, showing the possibility of an efficient inversion using (1) the migration of selected CIGs and COGs, (2) automatic picking on prior uncorrelated locally coherent events, (3) efficient computation of the gradient of the cost function via paraxial ray tracing from the picked events to the surface, and (4) a gradient‐type optimization algorithm for convergence.

Migration velocity analysis by double path-integral migration

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCA225-WCA231 ◽  
Author(s):  
Jörg Schleicher ◽  
Jessé C. Costa
Keyword(s):  
Path Integral ◽  
Velocity Model ◽  
Migration Velocity ◽  
Quantitative Information ◽  
Velocity Analysis ◽  
Integral Imaging ◽  
Velocity Models ◽  
Migration Velocity Analysis ◽  
Velocity Information ◽  
Starting Model

The idea of path-integral imaging is to sum over the migrated images obtained for a set of migration velocity models. Velocities where common-image gathers align horizontally are stationary, thus favoring these images in the overall stack. The overall image forms with no knowledge of the true velocity model. However, the velocity information associated with the final image can be determined in the process. By executing the path-integral imaging twice and weighting one of the stacks with the velocity value, the stationary velocities that produce the final image can then be extracted by a division of the two images. The velocity extraction, interpola-tion, and smoothing can be done fully automatically, without the need for human interpretation or other intervention. A numerical example demonstrated that quantitative information about the migration velocity model can be determined by double path-integral migration. The so-obtained velocity model can then be used as a starting model for subsequent velocity analysis tools like migration velocity analysis or tomographic methods.

scholarly journals Joint migration velocity analysis of PP- and PS-waves for VTI media

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. WC123-WC135 ◽  
Author(s):  
Pengfei Cai ◽  
Ilya Tsvankin
Keyword(s):  
Grid Point ◽  
Transversely Isotropic ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Ocean Bottom ◽  
Velocity Models ◽  
The North ◽  
Migration Velocity Analysis ◽  
Vti Media

Combining PP-waves with mode-converted PS reflections in migration velocity analysis (MVA) can help build more accurate VTI (transversely isotropic with a vertical symmetry axis) velocity models. To avoid problems caused by the moveout asymmetry of PS-waves and take advantage of efficient MVA algorithms designed for pure modes, here we generate pure SS-reflections from PP and PS data using the [Formula: see text] method. Then the residual moveout in both PP and SS common-image gathers is minimized during iterative velocity updates. The model is divided into square cells, and the VTI parameters [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] are defined at each grid point. The objective function also includes the differences between the migrated depths of the same reflectors on the PP and SS sections. Synthetic examples confirm that 2D MVA of PP- and PS-waves may be able to resolve all four relevant parameters of VTI media if reflectors with at least two distinct dips are available. The algorithm is also successfully applied to a 2D line from 3D ocean-bottom seismic data acquired at Volve field in the North Sea. After the anisotropic velocity model has been estimated, accurate depth images can be obtained by migrating the recorded PP and PS data.

Tau migration and velocity analysis: Theory and synthetic examples

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1331-1339 ◽  
Author(s):  
Tariq Alkhalifah
Keyword(s):  
Time Domain ◽  
Synthetic Data ◽  
Velocity Model ◽  
Migration Velocity ◽  
Surface Velocity ◽  
Velocity Analysis ◽  
Near Surface ◽  
Prestack Migration ◽  
Interval Velocity ◽  
Migration Velocity Analysis

Prestack migration velocity analysis in the time domain reduces the velocity‐depth ambiguity usually hampering the performance of prestack depth‐migration velocity analysis. In prestack τ migration velocity analysis, we keep the interval velocity model and the output images in vertical time. This allows us to avoid placing reflectors at erroneous depths during the velocity analysis process and, thus, avoid slowing down its convergence to the true velocity model. Using a 1D velocity update scheme, the prestack τ migration velocity analysis performed well on synthetic data from a model with a complex near‐surface velocity. Accurate velocity information and images were obtained using this time‐domain method. Problems occurred only in resolving a thin layer where the low resolution and fold of the synthetic data made it practically impossible to estimate velocity accurately in this layer. This 1D approach also provided us reasonable results for synthetic data from the Marmousi model. Despite the complexity of this model, the τ domain implementation of the prestack migration velocity analysis converged to a generally reasonable result, which includes properly imaging the elusive top‐of‐the‐reservoir layer.

Multidip tomography formulation for migration velocity analysis

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. U1-U12 ◽  
Author(s):  
Raanan Dafni ◽  
Moshe Reshef
Keyword(s):  
Imaging System ◽  
Real Data ◽  
Velocity Model ◽  
Equation System ◽  
Migration Velocity ◽  
Opening Angle ◽  
Velocity Analysis ◽  
Migration Velocity Analysis ◽  
Seismic Image ◽  
Dip Angle

We have developed a new approach for migration velocity analysis by ray-based reflection tomography, formulated according to more than a single dip direction. It is suggested to use a summation-free subsurface imaging system in the angle domain for generating multiparameter common image gathers through depth migration. Each gather associated with this system comprised dip-dependent opening-angle images contributed from the prestack data. Independent moveout information, coming from either a specular or nonspecular dip direction, was extracted inside these gathers to allow the entire scattered field to be involved in the velocity model optimization. By obeying the linear tomographic principle, a multidip tomography system was set to include imaging errors from specular and nonspecular directions. The updated velocity model was reconstructed by a least-squares inverse solution of the multidip tomographic equation system. Providing additional moveout from the migration’s dip, other than the specular one, was believed to be essential because the seismic data were misplaced in the image space while applying depth imaging by an erroneous velocity model. It might make the determination of a clear specular orientation, usually from the seismic image itself, misleading or ambiguous. The conversion of depth moveout into traveltime error along nonspecular rays was done according to an analytic mechanism, derived in the angle domain. The proposed analysis of migration errors by a multiple dip-angle orientation is demonstrated via the multidip tomography formulation by 2D synthetic and real data examples. It seems to be more efficient, as accurate and reliable as the conventional analysis, and to be better able to determine the ill-posed conditioning of the tomographic inversion.

Dip correction for coherence-based time migration velocity analysis

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. S41-S48 ◽  
Author(s):  
Jörg Schleicher ◽  
Ricardo Biloti
Keyword(s):  
Optimal Parameter ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
Processing Step ◽  
Best Fitting ◽  
Two Parameter

Migration velocity analysis (MVA) is a seismic processing step that aims to translate residual moveout in an image gather after migration with an erroneous velocity model into velocity updates. An analysis of the position of a reflection event in an image gather after migration with an incorrect velocity allows us to extend the original coherence-based MVA approach to dipping reflectors. The extended MVA technique includes the reflector dip which is treated as an additional search parameter that is to be detected together with the velocity updating factor. Both parameters are searched for simultaneously by the application of two-parameter search techniques. The search consists of determining trial curves as a function of the search parameters and stacking the migrated data along these curves. The highest coherence determines the best-fitting curve and thus the optimal parameter pair. A numerical example demonstrates that the additional search parameter improves the quality of the velocity updates, thus requiring less iterations in the MVA.

Residual migration‐velocity analysis in the plane‐wave domain

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1258-1269 ◽  
Author(s):  
Junru Jiao ◽  
Paul L. Stoffa ◽  
Mrinal K. Sen ◽  
Roustam K. Seifoullaev
Keyword(s):  
Plane Wave ◽  
Field Data ◽  
Velocity Model ◽  
Migration Velocity ◽  
New Method ◽  
Velocity Analysis ◽  
Top Down ◽  
Velocity Models ◽  
Migration Velocity Analysis ◽  
Interval Velocities

Over the last few years, migration‐velocity analysis methods have been developed for 2‐D and 3‐D models by extending the assumptions and approximations used for rms velocity models. Computational requirements for these analyses have increased dramatically because top‐down layer‐stripping migration is needed to derive interval velocities directly instead of using rms velocities and then converting into interval velocities. We establish exact equations for 1‐D and 2‐D residual velocity analysis in the depth‐plane‐wave domain and use these in an iterative and interactive migration velocity analysis program. The new method updates interval velocities directly in a top‐down residual‐difference correction for all layers after prestack depth migration instead of top‐down layer‐stripping migration followed by residual analysis. This makes the new method a suitable tool for migration velocity analysis, especially for 3‐D surveys. We test the method on synthetic and field data. The field data results show that a reasonable velocity model is obtained and most common image gathers are correctly imaged using no more than four iterations.

Numeric implementation of wave-equation migration velocity analysis operators

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE145-VE159 ◽  
Author(s):  
Paul Sava ◽  
Ioan Vlad
Keyword(s):  
Wave Equation ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Velocity Models ◽  
Image Optimization ◽  
Migration Velocity Analysis ◽  
Zero Offset ◽  
Wavefield Extrapolation

Wave-equation migration velocity analysis (MVA) is a technique similar to wave-equation tomography because it is designed to update velocity models using information derived from full seismic wavefields. On the other hand, wave-equation MVA is similar to conventional, traveltime-based MVA because it derives the information used for model updates from properties of migrated images, e.g., focusing and moveout. The main motivation for using wave-equation MVA is derived from its consistency with the corresponding wave-equation migration, which makes this technique robust and capable of handling multipathing characterizing media with large and sharp velocity contrasts. The wave-equation MVA operators are constructed using linearizations of conventional wavefield extrapolation operators, assuming small perturbations relative to the background velocity model. Similar to typical wavefield extrapolation operators, the wave-equation MVA operators can be implemented in the mixed space-wavenumber domain using approximations of differentorders of accuracy. As for wave-equation migration, wave-equation MVA can be formulated in different imaging frameworks, depending on the type of data used and image optimization criteria. Examples of imaging frameworks correspond to zero-offset migration (designed for imaging based on focusing properties of the image), survey-sinking migration (designed for imaging based on moveout analysis using narrow-azimuth data), and shot-record migration (also designed for imaging based on moveout analysis, but using wide-azimuth data). The wave-equation MVA operators formulated for the various imaging frameworks are similar because they share elements derived from linearizations of the single square-root equation. Such operators represent the core of iterative velocity estimation based on diffraction focusing or semblance analysis, and their applicability in practice requires efficient and accurate implementation. This tutorial concentrates strictly on the numeric implementation of those operators and not on their use for iterative migration velocity analysis.

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