Traveltime approximation for converted waves in elastic orthorhombic media

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. C229-C237 ◽  
Author(s):  
Shibo Xu ◽  
Alexey Stovas
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Processing Parameters ◽  
Model Parameters ◽  
Seismic Data Processing ◽  
Converted Wave ◽  
Rational Form ◽  
Time Migration ◽  
Taylor Series Approximation ◽  
Anisotropy Parameters

The moveout approximations are commonly used in seismic data processing such as velocity analysis, modeling, and time migration. The anisotropic effect is very obvious for a converted wave when estimating the physical and processing parameters from the real data. To approximate the traveltime in an elastic orthorhombic (ORT) medium, we defined an explicit rational-form approximation for the traveltime of the converted [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-waves. To obtain the expression of the coefficients, the Taylor-series approximation is applied in the corresponding vertical slowness for three pure-wave modes. By using the effective model parameters for [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-waves, the coefficients in the converted-wave traveltime approximation can be represented by the anisotropy parameters defined in the elastic ORT model. The accuracy in the converted-wave traveltime for three ORT models is illustrated in numerical examples. One can see from the results that, for converted [Formula: see text]- and [Formula: see text]-waves, our rational-form approximation is very accurate regardless of the tested ORT model. For a converted [Formula: see text]-wave, due to the existence of cusps, triplications, and shear singularities, the error is relatively larger compared with PS-waves.

Estimation of the conversion point position in elastic orthorhombic media

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. C15-C25 ◽  
Author(s):  
Shibo Xu ◽  
Alexey Stovas
Keyword(s):  
Real Data ◽  
Processing Parameters ◽  
Position Estimation ◽  
Point Estimation ◽  
Radial Coordinate ◽  
Converted Wave ◽  
Rational Form ◽  
Point Position ◽  
Conversion Point ◽  
Wave Modes

Determination of the conversion point position is very important to carry out seismic processing in the common conversion point gather of converted wave data. The anisotropic effect is very obvious for a converted wave when estimating the physical and processing parameters from real data. To estimate the conversion point in an elastic orthorhombic (ORT) medium, we have defined an explicit rational form approximation for the radial coordinate of the conversion point for converted [Formula: see text], [Formula: see text], and [Formula: see text] waves. To obtain the approximation coefficients, the Taylor series approximation in the corresponding vertical slowness for three pure wave modes is applied. The coefficients in our proposed approximation are computed within two vertical symmetry planes. The difference between the acquisition azimuth and the azimuth of the conversion point position is analyzed for different combinations of the wave modes. The accuracy of the conversion point position estimation for three ORT models is illustrated in the numerical examples. One can see from the results that for converted [Formula: see text] and [Formula: see text] waves, our approximation is very accurate in estimating the conversion point position regardless of the tested ORT model. For a converted [Formula: see text] wave, due to the existence of cusps, triplications, and shear singularities, the error in conversion point estimation is relatively larger compared with PS-waves in the vicinity of the singularity point.

scholarly journals OPTIMALISASI PENCITRAAN STRUKTUR BAWAH PERMUKAAN MENGGUNAKAN METODE KIRCHHOFF PRE-STACK TIME MIGRATION PADA DATA SEISMIK LAUT WETAR

2020 ◽  
Vol 6 (2) ◽  
pp. 101-112
Author(s):  
Syamsurijal Rasimeng ◽  
Amelia Isti Ekarena ◽  
Bagus Sapto Mulyanto ◽  
Subarsyah Subarsyah ◽  
Andrian Wilyan Djaja
Keyword(s):  
Data Processing ◽  
Cross Section ◽  
Seismic Data ◽  
Processing Parameters ◽  
Geological Structure ◽  
Diffraction Effect ◽  
Seismic Data Processing ◽  
Existing Problems ◽  
Time Migration ◽  
Diffraction Effects

Migration is one of the stages in seismic data processing aimed at returning the diffraction effect to the actual reflector point. The processing of a seismic data is adjusted to the existing problems in the data itself, so the accuracy in using the migration technique and determination of data processing parameters greatly affects the resulting seismic cross-section. Kirchhoff Pre-Stack Time Migration is one of the most used migration methods in seismic data processing because it shows better results than conventional stacking methods. The parameters that need to be noticed in the Kirchhoff migration are the migration aperture values. Based on this, variations of migration aperture values used are 75 m, 200 m and 512.5 m. The 512.5-m aperture migration value shows the best seismic cross-section results. This is evidenced by the capability in eliminating bowtie effects around CDP 600 up to CDP 800, eliminating diffraction effects around CDP 3900 to CDP 4050, and showing a seismic cross-section with better lateral resolution compared to the migration value of the aperture of 75 m and 200 m. Based on the seismic cross-section of migration results, the geological structure that can be identified is a fault that found in some CDP.

Wave-equation angle-domain common-image gathers for converted waves

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. S17-S26 ◽  
Author(s):  
Daniel A. Rosales ◽  
Sergey Fomel ◽  
Biondo L. Biondi ◽  
Paul C. Sava
Keyword(s):  
Seismic Data ◽  
Incidence Angle ◽  
Velocity Ratio ◽  
Real Data ◽  
Single Mode ◽  
Practical Application ◽  
Converted Wave ◽  
Reflection Angle ◽  
Wavefield Extrapolation ◽  
Local Image

Wavefield-extrapolation methods can produce angle-domain common-image gathers (ADCIGs). To obtain ADCIGs for converted-wave seismic data, information about the image dip and the P-to-S velocity ratio must be included in the computation of angle gathers. These ADCIGs are a function of the half-aperture angle, i.e., the average between the incidence angle and the reflection angle. We have developed a method that exploits the robustness of computing 2D isotropic single-mode ADCIGs and incorporates both the converted-wave velocity ratio [Formula: see text] and the local image dip field. It also maps the final converted-wave ADCIGs into two ADCIGs, one a function of the P-incidence angle and the other a function of the S-reflection angle. Results with both synthetic and real data show the practical application for converted-wave ADCIGs. The proposed approach is valid in any situation as long as the migration algorithm is based on wavefield downward continuation and the final prestack image is a function of the horizontal subsurface offset.

2-D continuation operators and their applications

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1846-1858 ◽  
Author(s):  
Claudio Bagaini ◽  
Umberto Spagnolini
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Integral Form ◽  
Amplitude Distribution ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Seismic Data Processing ◽  
Analysis Method ◽  
Standard Tool ◽  
Zero Offset

Continuation to zero offset [better known as dip moveout (DMO)] is a standard tool for seismic data processing. In this paper, the concept of DMO is extended by introducing a set of operators: the continuation operators. These operators, which are implemented in integral form with a defined amplitude distribution, perform the mapping between common shot or common offset gathers for a given velocity model. The application of the shot continuation operator for dip‐independent velocity analysis allows a direct implementation in the acquisition domain by exploiting the comparison between real data and data continued in the shot domain. Shot and offset continuation allow the restoration of missing shot or missing offset by using a velocity model provided by common shot velocity analysis or another dip‐independent velocity analysis method.

Traveltime and relative geometric spreading approximation in elastic orthorhombic medium

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. C153-C162 ◽  
Author(s):  
Shibo Xu ◽  
Alexey Stovas ◽  
Hitoshi Mikada
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
P Wave ◽  
Form Expression ◽  
Amplitude Variation ◽  
Rational Form ◽  
Amplitude Variation With Offset ◽  
Practical Applications ◽  
Numerical Tests ◽  
Geometric Spreading

Wavefield properties such as traveltime and relative geometric spreading (traveltime derivatives) are highly essential in seismic data processing and can be used in stacking, time-domain migration, and amplitude variation with offset analysis. Due to the complexity of an elastic orthorhombic (ORT) medium, analysis of these properties becomes reasonably difficult, where accurate explicit-form approximations are highly recommended. We have defined the shifted hyperbola form, Taylor series (TS), and the rational form (RF) approximations for P-wave traveltime and relative geometric spreading in an elastic ORT model. Because the parametric form expression for the P-wave vertical slowness in the derivation is too complicated, TS (expansion in offset) is applied to facilitate the derivation of approximate coefficients. The same approximation forms computed in the acoustic ORT model also are derived for comparison. In the numerical tests, three ORT models with parameters obtained from real data are used to test the accuracy of each approximation. The numerical examples yield results in which, apart from the error along the y-axis in ORT model 2 for the relative geometric spreading, the RF approximations all are very accurate for all of the tested models in practical applications.

Borehole-Driven 3D Surface Seismic Data Processing Using DAS-VSP Data

2021 ◽  
Author(s):  
Gang Yu ◽  
Junjun Wu ◽  
Yuanzhong Chen ◽  
Ximing Wang
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Seismic Data ◽  
Field Data ◽  
Seismic Data Processing ◽  
3D Surface ◽  
Data Volume ◽  
Vsp Data ◽  
Seismic Data Acquisition ◽  
Anisotropy Parameters

Abstract A 3D surface seismic data acquisition project was conducted simultaneously with 3D DAS-VSP data acquisition in one well in Jilin Oilfield of Northen China. The 3D surface seismic data acquisition project covered an area of 75 km2, and one borehole (DS32-3) and an armoured optical cable with high temperature single mode fiber were used to acquire the 3D DAS-VSP data simultaneously when the crew was acquiring the 3D surface seismic data. The simultaneously acquired 3D DAS-VSP data were used to extract formation velocity, deconvolution operator, absorption, attenuation (Q value), anisotropy parameters (η, δ, ε) as wel as enhanced the surface seismic data processing including velocity model calibration and modification, static correction, deconvolution, demultiple processing, high frequency restoration, anisotropic migration, and Q-compensation or Q-migration. In this project, anisotropic migration, Q-migration was conducted with the anisotropy parameters (η, δ, ε) data volume and enhanced Q-field data volume obtained from the joint inversion of both the near surface 3D Q-field data volume from uphole data and the mid-deep layer Q-field data volume from all available VSP data in the 3D surface seismic surveey area. The anosotropic migration and Q-migration results show much sharper and focussed faults and and clearer subsutface structure.

Image-guided raytracing and its applications

Geophysics ◽  
2021 ◽  
pp. 1-44
Author(s):  
Eduardo Silva ◽  
Jessé Costa ◽  
Jörg Schleicher
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Eikonal Equation ◽  
Real Data ◽  
Data Sets ◽  
Seismic Data Processing ◽  
Fast Marching ◽  
Image Guided ◽  
Ray Density ◽  
Original Motivation

Eikonal solvers have found important applications in seismic data processing and in-version, the so-called image-guided methods. To this day in image-guided applications, thesolution of the eikonal equation is implemented using partial-differential-equationsolvers, such as fast-marching or fast-sweeping methods. We show that alternatively, onecan numerically integrate the dynamic Hamiltonian system defined by the image-guidedeikonal equation and reconstruct the solution with image-guided rays. We present interest-ing applications of image-guided raytracing to seismic data processing, demonstrating theuse of the resulting rays in image-guided interpolation and smoothing, well-log interpola-tion, image flattening, and residual-moveout picking. Some of these applications make useof properties of the raytracing system that are not directly obtained by eikonal solvers, suchas ray position, ray density, wavefront curvature, and ray curvature. These ray propertiesopen space for a different set of applications of the image-guided eikonal equation, beyondthe original motivation of accelerating the construction of minimum distance tables. Westress that image-guided raytracing is an embarrassingly parallel problem, which makes itsimplementation highly efficient on massively parallel platforms. Image-guided raytracing isadvantageous for most applications involving the tracking of seismic events and imaging-guided interpolation. Our numerical experiments using synthetic and real data sets showthe efficiency and robustness of image-guided rays for the selected applications.

AVO inversion and poroelasticity with P- and S-wave moduli

Geophysics ◽  
2012 ◽  
Vol 77 (6) ◽  
pp. N17-N24 ◽  
Author(s):  
Zhaoyun Zong ◽  
Xingyao Yin ◽  
Guochen Wu
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
P Wave ◽  
Model Parameters ◽  
Initial Model ◽  
S Wave ◽  
Data Set ◽  
Statistical Probability ◽  
Avo Inversion ◽  
Poroelasticity Theory

The fluid term in the Biot-Gassmann equation plays an important role in reservoir fluid discrimination. The density term imbedded in the fluid term, however, is difficult to estimate because it is less sensitive to seismic amplitude variations. We combined poroelasticity theory, amplitude variation with offset (AVO) inversion, and identification of P- and S-wave moduli to present a stable and physically meaningful method to estimate the fluid term, with no need for density information from prestack seismic data. We used poroelasticity theory to express the fluid term as a function of P- and S-wave moduli. The use of P- and S-wave moduli made the derivation physically meaningful and natural. Then we derived an AVO approximation in terms of these moduli, which can then be directly inverted from seismic data. Furthermore, this practical and robust AVO-inversion technique was developed in a Bayesian framework. The objective was to obtain the maximum a posteriori solution for the P-wave modulus, S-wave modulus, and density. Gaussian and Cauchy distributions were used for the likelihood and a priori probability distributions, respectively. The introduction of a low-frequency constraint and statistical probability information to the objective function rendered the inversion more stable and less sensitive to the initial model. Tests on synthetic data showed that all the parameters can be estimated well when no noise is present and the estimated P- and S-wave moduli were still reasonable with moderate noise and rather smooth initial model parameters. A test on a real data set showed that the estimated fluid term was in good agreement with the results of drilling.

Seismic data processing in vertically inhomogeneous TI media

Geophysics ◽  
1997 ◽  
Vol 62 (2) ◽  
pp. 662-675 ◽  
Author(s):  
Tariq Alkhalifah
Keyword(s):  
Seismic Data ◽  
Transversely Isotropic ◽  
Inhomogeneous Media ◽  
P Wave ◽  
Seismic Data Processing ◽  
Time Migration ◽  
Processing Data ◽  
Two Parameters ◽  
Ti Media ◽  
Ray Parameter

The first and most important step in processing data in transversely isotropic (TI) media for which velocities vary with depth is parameter estimation. The multilayer normal‐moveout (NMO) equation for a dipping reflector provides the basis for extending the TI velocity analysis of Alkhalifah and Tsvankin to vertically inhomogeneous media. This NMO equation is based on a root‐mean‐square (rms) average of interval NMO velocities that correspond to a single ray parameter, that of the dipping event. Therefore, interval NMO velocities [including the normal‐moveout velocity for horizontal events, [Formula: see text]] can be extracted from the stacking velocities using a Dix‐type differentiation procedure. On the other hand, η, which is a key combination of Thomsen's parameters that time‐related processing relies on, is extracted from the interval NMO velocities using a homogeneous inversion within each layer. Time migration, like dip moveout, depends on the same two parameters in vertically inhomogeneous media, namely [Formula: see text] and η, both of which can vary with depth. Therefore, [Formula: see text] and ε estimated using the dip dependency of P‐wave moveout velocity can be used for TI time migration. An application of anisotropic processing to seismic data from offshore Africa demonstrates the importance of considering anisotropy, especially as it pertains to focusing and imaging of dipping events.

Sign in / Sign up

Export Citation Format

Share Document