Selective-correlation velocity analysis

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. U11-U19 ◽  
Author(s):  
Ken Larner ◽  
Valmore Celis
Keyword(s):  
Time Domain ◽  
Random Noise ◽  
Computational Cost ◽  
Threshold Value ◽  
Resolving Power ◽  
Velocity Analysis ◽  
Reflection Time ◽  
Offset Time ◽  
Zero Offset ◽  
Velocity Spectra

Increased resolution in computed velocity spectra aids in distinguishing between neighboring primary events from reflectors with conflicting dip and in identifying primaries in the presence of multiples. The transformation from the offset and reflection-time domain to the stacking-velocity and zero-offset-time domain can be achieved using any of several coherence measures based on crosscorrelations among traces in a common-midpoint (CMP) gather or a common-image gather (CIG). Use of just selected subsets of crosscorrelations rather than all possible ones in a gather can improve both the reliability and resolution of velocity analysis. In selective-correlation velocity analysis, we include in the summation only crosscorrelations for those pairs of traces with relative differential moveout of reflections exceeding a chosen threshold value. Comparisons of performance on CMP gathers, both synthetic and field-data, show that selective-correlation velocity analysis considerably enhances the resolving power of velocity spectra over that of conventional crosscorrelation sum (normalized or unnormalized) in the presence of closely interfering reflections, statics distortions, and random noise, at no sacrifice in quality of results, and does so at computational cost comparable to that for conventional velocity analysis.

3D generalized nonhyperboloidal moveout approximation

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. C49-C59 ◽  
Author(s):  
Yanadet Sripanich ◽  
Sergey Fomel ◽  
Alexey Stovas ◽  
Qi Hao
Keyword(s):  
Time Domain ◽  
Functional Form ◽  
Seismic Imaging ◽  
High Accuracy ◽  
Velocity Analysis ◽  
Zero Offset ◽  
Independent Parameters

Moveout approximations are commonly used in velocity analysis and time-domain seismic imaging. We revisit the previously proposed generalized nonhyperbolic moveout approximation and develop its extension to the 3D multiazimuth case. The advantages of the generalized moveout approximation are its high accuracy and its ability to reduce to several other known approximations with particular choices of parameters. The proposed 3D functional form involves 17 independent parameters instead of five as in the 2D case. These parameters can be defined by zero-offset traveltime attributes and four additional far-offset rays. In our tests, the proposed approximation achieves significantly higher accuracy than previously proposed 3D approximations.

Generalized nonhyperbolic moveout approximation

Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. U9-U18 ◽  
Author(s):  
Sergey Fomel ◽  
Alexey Stovas
Keyword(s):  
Functional Form ◽  
Velocity Analysis ◽  
Numerical Examples ◽  
Time Migration ◽  
Analytical Approximations ◽  
Offset Time ◽  
Zero Offset ◽  
Two Parameters

Reflection moveout approximations are commonly used for velocity analysis, stacking, and time migration. A novel functional form for approximating the moveout of reflection traveltimes at large offsets is introduced. In comparison with the classic hyperbolic approximation, which uses only two parameters (zero-offset time and moveout velocity), this form involves five parameters that can be determined, in a known medium, from zero-offset computations and from tracing one nonzero-offset ray. It is called a generalized approximation because it reduces to some known three-parameter forms with a particular choice of coefficients. By testing the accuracy of the proposed approximation with analytical and numerical examples, the new approximation is shown to bring an improvement in accuracy of several orders of magnitude compared to known analytical approximations, which makes it as good as exact for many practical purposes.

Description of a scanless method of stacking velocity analysis

Geophysics ◽  
1993 ◽  
Vol 58 (11) ◽  
pp. 1596-1606 ◽  
Author(s):  
Hans J. Tieman
Keyword(s):  
Empirical Studies ◽  
Real Data ◽  
Velocity Variation ◽  
Velocity Analysis ◽  
Velocity Control ◽  
Lateral Velocity ◽  
Interval Velocity ◽  
Offset Time ◽  
Zero Offset ◽  
Traditional Approaches

Stacking velocities can be directly estimated from seismic data without recourse to a multivelocity stack and subsequent search techniques that many current procedures use. This is done as follows: (1) apply NMO to the data (over a window, for a particular common midpoint) using initial estimates for zero offset time and velocity; (2) produce two stacks by summing the data over offset after applying different weighting functions; (3) cross correlate the two stacks; and (4) translate the lag into velocity and time updates. The procedure is iterated until convergence has occurred. Referred to as ARAMVEL (U.S. Patent No. 4,813,027), the method is best implemented as an interactive continuous velocity analysis. Although very simple, both empirical studies and theoretical analysis have shown that it determines velocities more accurately than more traditional approaches based on a scan approach. Convergence is fast, with only one or two iterations usually necessary. The method is robust, as only approximate information is necessary initially. Results with real data show that the method can economically give the detailed velocity control necessary for processing data from areas with considerable lateral velocity variation, as well as provide traveltime information that can be used for sophisticated inversion into interval velocity and depth.

scholarly journals Velocity analysis based on minimum entropy

Geophysics ◽  
1984 ◽  
Vol 49 (12) ◽  
pp. 2132-2142 ◽  
Author(s):  
D. De Vries ◽  
A. J. Berkhout
Keyword(s):  
Velocity Estimation ◽  
Resolving Power ◽  
Velocity Analysis ◽  
Minimum Entropy ◽  
Estimation Techniques ◽  
Velocity Error ◽  
Seismic Resolution ◽  
Velocity Information ◽  
Zero Offset

Seismic resolution is determined by the sparsity of reflection events together with the dispersion of the wavelets representing those events. In this paper, minimum entropy (ME) norms are introduced as a measure of spatial resolving power. It is shown that the lateral dispersion of inverted diffractor responses (inverted spatial wavelets) increases with increasing velocity error. Using this property, minimum entropy velocity analysis (MEVA) is proposed to extract velocity information from diffraction energy. MEVA can be successfully applied to zero‐offset (including poststack) data and common‐offset data with a sufficient amount of diffraction energy. In addition, MEVA can be used as an alternative to existing CMP velocity estimation techniques.

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.

An inverse scattering method in time-domain electromagnetics

1981 ◽  
Vol 59 (10) ◽  
pp. 1348-1353
Author(s):  
Sujeet K. Chaudhuri
Keyword(s):  
Inverse Scattering ◽  
Time Domain ◽  
Random Noise ◽  
Power Series Expansion ◽  
Inverse Scattering Method ◽  
Wave Number ◽  
Moment Condition ◽  
Scattering Method ◽  
Fourth Moment ◽  
Rayleigh Coefficient

An inverse scattering model, based on the time-domain concepts of electromagnetic theory is developed. Using the first five (zeroth to fourth) moment condition integrals, the Rayleigh coefficient and the next higher order nonzero coefficient of the power series expansion in k (wave number) of the object backscattering response are recovered. The Rayleigh coefficient and the other coefficient thus recovered are used (with the ellipsoidal assumption for the object shape) to determine the dimensions and orientation of the object.Some numerical results of the application of this coefficient recovery technique to conducting ellipsoidal scatterers are presented. The performance of the software system in the presence of normally distributed random noise is also studied.

scholarly journals Study on the Velocity Analysis Method of Low SNR Seismic Data

2021 ◽  
Vol 11 (1) ◽  
pp. 78
Author(s):  
Jianbo He ◽  
Zhenyu Wang ◽  
Mingdong Zhang
Keyword(s):  
Seismic Data ◽  
Fresnel Zone ◽  
Signal To Noise Ratio ◽  
Normal Wave ◽  
Curvature Radius ◽  
Velocity Spectrum ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Zero Offset

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.

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