Explicit analytic expression for normal moveout from horizontal and dipping reflectors in weakly anisotropic media of arbitrary symmetry type

Geophysics ◽  
2000 ◽  
Vol 65 (4) ◽  
pp. 1294-1304 ◽  
Author(s):  
P. N. J. Rasolofosaon

When processing and inverting seismic reflection data, the NMO velocity must be correctly described, taking into account realistic situations such as the presence of anisotropy and dipping reflectors. Some dip‐moveout (DMO) algorithms have been developed, such as Tsvankin’s analytic formula. It describes the anisotropy‐induced distortions in the classical isotropic cosine of dip dependence of the NMO velocity. However, it is restricted to the vertical symmetry planes of anisotropic media, so the technique is unsuitable for the azimuthal inspection of sedimentary rocks, either with horizontal bedding and vertical fractures or with dipping bedding but no fractures. However, under the weak anisotropy approximation the deviations of the rays from a vertical plane can be neglected for the traveltimes computation, and the equation can still be applicable. Based on this approach, an explicit analytic expression for the P-wave NMO velocity in the presence of horizontal or dipping reflectors in media exhibiting the most general symmetry type (triclinic) is obtained in this work. If the medium exhibits a horizontal symmetry plane, the concise DMO equations are formally identical to Tsvankin’s except that the parameters δ and ε are not constant but depend on the azimuth ψ Physically, δ(ψ) is the deviation from the vertical P-wave velocity of the P-wave NMO velocity for a horizontal reflector normalized by the vertical P-wave velocity for the azimuth ψ. The function ε(ψ) has the same definition as δ(ψ) except that the P-wave NMO velocity is replaced by the horizontal P-wave velocity. Both depend linearly on (1) new dimensionless anisotropy parameters and (2) generalizing to arbitrary symmetry the transversely isotropic parameters δ and ε. In the most general symmetry case (triclinic), an additional term to the DMO formula is necessary. The numerical examples, based on experimental data in rocks, show two things. First, the magnitude of the DMO errors induced by anisotropy depends primarily on the absolute value of ε(ψ) − δ(ψ) and not on the individual values of ε(ψ) and δ(ψ), which is a direct consequence of the similarity between Tsvankin’s equation and the equation presented here. Second, the anisotropy‐induced DMO correction can be significant even in the presence of moderate anisotropy and can exhibit complex azimuthal dependence.

Geophysics ◽  
1986 ◽  
Vol 51 (10) ◽  
pp. 1893-1903 ◽  
Author(s):  
Albert Tarantola

The problem of interpretation of seismic reflection data can be posed with sufficient generality using the concepts of inverse theory. In its roughest formulation, the inverse problem consists of obtaining the Earth model for which the predicted data best fit the observed data. If an adequate forward model is used, this best model will give the best images of the Earth’s interior. Three parameters are needed for describing a perfectly elastic, isotropic, Earth: the density ρ(x) and the Lamé parameters λ(x) and μ(x), or the density ρ(x) and the P-wave and S-wave velocities α(x) and β(x). The choice of parameters is not neutral, in the sense that although theoretically equivalent, if they are not adequately chosen the numerical algorithms in the inversion can be inefficient. In the long (spatial) wavelengths of the model, adequate parameters are the P-wave and S-wave velocities, while in the short (spatial) wavelengths, P-wave impedance, S-wave impedance, and density are adequate. The problem of inversion of waveforms is highly nonlinear for the long wavelengths of the velocities, while it is reasonably linear for the short wavelengths of the impedances and density. Furthermore, this parameterization defines a highly hierarchical problem: the long wavelengths of the P-wave velocity and short wavelengths of the P-wave impedance are much more important parameters than their counterparts for S-waves (in terms of interpreting observed amplitudes), and the latter are much more important than the density. This suggests solving the general inverse problem (which must involve all the parameters) by first optimizing for the P-wave velocity and impedance, then optimizing for the S-wave velocity and impedance, and finally optimizing for density. The first part of the problem of obtaining the long wavelengths of the P-wave velocity and the short wavelengths of the P-wave impedance is similar to the problem solved by present industrial practice (for accurate data interpretation through velocity analysis and “prestack migration”). In fact, the method proposed here produces (as a byproduct) a generalization to the elastic case of the equations of “prestack acoustic migration.” Once an adequate model of the long wavelengths of the P-wave velocity and of the short wavelengths of the P-wave impedance has been obtained, the data residuals should essentially contain information on S-waves (essentially P-S and S-P converted waves). Once the corresponding model of S-wave velocity (long wavelengths) and S-wave impedance (short wavelengths) has been obtained, and if the remaining residuals still contain information, an optimization for density should be performed (the short wavelengths of impedances do not give independent information on density and velocity independently). Because the problem is nonlinear, the whole process should be iterated to convergence; however, the information from each parameter should be independent enough for an interesting first solution.


Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 345-351 ◽  
Author(s):  
Geoff J.M. Moret ◽  
William P. Clement ◽  
Michael D. Knoll ◽  
Warren Barrash

P‐wave velocity information obtained from vertical seismic profiles (VSPs) can be useful in imaging subsurface structure, either by directly detecting changes in the subsurface or as an aid to the interpretation of seismic reflection data. In the shallow subsurface, P‐wave velocity can change by nearly an order of magnitude over a short distance, so curved rays are needed to accurately model VSP traveltimes. We used a curved‐ray inversion to estimate the velocity profile and the discrepancy principle to estimate the data noise level and to choose the optimum regularization parameter. The curved‐ray routine performed better than a straight‐ray inversion for synthetic models containing high‐velocity contrasts. The application of the inversion to field data produced a velocity model that agreed well with prior information. These results show that curved‐ray inversion should be used to obtain velocity information from VSPs in the shallow subsurface.


2021 ◽  
Author(s):  
Dariusz Chlebowski ◽  
Zbigniew Burtan

AbstractA variety of geophysical methods and analytical modeling are applied to determine the rockburst hazard in Polish coal mines. In particularly unfavorable local conditions, seismic profiling, active/passive seismic tomography, as well as analytical state of stress calculating methods are recommended. They are helpful in verifying the reliability of rockburst hazard forecasts. In the article, the combined analysis of the state of stress determined by active seismic tomography and analytical modeling was conducted taking into account the relationship between the location of stress concentration zones and the level of rockburst hazard. A longwall panel in the coal seam 501 at a depth of ca.700 m in one of the hard coal mines operating in the Upper Silesian Coal Basin was a subject of the analysis. The seismic tomography was applied for the reconstruction of P-wave velocity fields. The analytical modeling was used to calculate the vertical stress states basing on classical solutions offered by rock mechanics. The variability of the P-wave velocity field and location of seismic anomaly in the coal seam in relation to the calculated vertical stress field arising in the mined coal seam served to assess of rockburst hazard. The applied methods partially proved their adequacy in practical applications, providing valuable information on the design and performance of mining operations.


2021 ◽  
pp. 228973
Author(s):  
Junhao Qu ◽  
Stephen S. Gao ◽  
Changzai Wang ◽  
Kelly H. Liu ◽  
Shaohui Zhou ◽  
...  

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