scholarly journals Traveltime-based true-amplitude migration

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. S251-S259 ◽  
Author(s):  
Claudia Vanelle ◽  
Miriam Spinner ◽  
Thomas Hertweck ◽  
Christoph Jäger ◽  
Dirk Gajewski
Keyword(s):  
Image Quality ◽  
Ray Tracing ◽  
Seismic Reflection ◽  
Real Data ◽  
Weight Functions ◽  
Velocity Models ◽  
Kirchhoff Migration ◽  
Alternative Approach ◽  
Dynamic Ray Tracing ◽  
Structural Image

True-amplitude Kirchhoff migration (TAKM) is an important tool in seismic-reflection imaging. In addition to a structural image, it leads to reflectivity maps of the subsurface. TAKM is carried out in terms of a weighted diffraction stack where the weight functions are computed with dynamic ray tracing (DRT) in addition to the diffraction traveltimes. DRT, however, is time-consuming and imposes restrictions on the velocity models, which are not always acceptable. An alternative approach to TAKM is proposed in which the weight functions are directly determined from the diffraction traveltimes. Because other methods exist for the generation of traveltimes, this approach is not limited by the requirements for DRT. Applications to a complex synthetic model and real data demonstrate that the image quality and accuracy of the reconstructed amplitudes are equivalent to those obtained from TAKM with DRT-generated weight functions.

True-amplitude Kirchhoff depth migration in anisotropic media: The traveltime-based approach

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. WC33-WC39 ◽  
Author(s):  
Claudia Vanelle ◽  
Dirk Gajewski
Keyword(s):  
Ray Tracing ◽  
Reservoir Characterization ◽  
Seismic Anisotropy ◽  
Anisotropic Media ◽  
Weight Functions ◽  
Depth Migration ◽  
Dynamic Ray Tracing ◽  
Isotropic Media ◽  
Structural Image ◽  
Kirchhoff Depth Migration

True-amplitude Kirchhoff depth migration is a classic tool in seismic imaging. In addition to a focused structural image, it also provides information on the strength of the reflectors in the model, leading to estimates of the shear properties of the subsurface. This information is a key feature not only for reservoir characterization, but it is also important for detecting seismic anisotropy. If anisotropy is present, it needs to be accounted for during the migration. True-amplitude Kirchhoff depth migration is carried out in terms of a weighted diffraction stack. Expressions for suitable weight functions exist in anisotropic media. However, the conventional means of computing the weights is based on dynamic ray tracing, which has high requirements on the smoothness of the underlying model. We developed a method for the computation of the weight functions that does not require dynamic ray tracing because all necessary quantities are determined from traveltimes alone. In addition, the method led to considerable savings in computational costs. This so-called traveltime-based strategy was already introduced for isotropic media. We extended the strategy to incorporate anisotropy. For verification purposes and comparison to analytic references, we evaluated 2.5D migration examples for [Formula: see text] and [Formula: see text] reflections. Our results confirmed the high image quality and the accuracy of the reconstructed reflectivities.

Prestack Gaussian-beam depth migration in anisotropic media

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. S133-S138 ◽  
Author(s):  
Tianfei Zhu ◽  
Samuel H. Gray ◽  
Daoliu Wang
Keyword(s):  
Gaussian Beam ◽  
Ray Tracing ◽  
Synthetic Data ◽  
Transversely Isotropic ◽  
Anisotropic Media ◽  
Elastic Parameters ◽  
Depth Migration ◽  
Kirchhoff Migration ◽  
Beam Depth ◽  
Dynamic Ray Tracing

Gaussian-beam depth migration is a useful alternative to Kirchhoff and wave-equation migrations. It overcomes the limitations of Kirchhoff migration in imaging multipathing arrivals, while retaining its efficiency and its capability of imaging steep dips with turning waves. Extension of this migration method to anisotropic media has, however, been hampered by the difficulties in traditional kinematic and dynamic ray-tracing systems in inhomogeneous, anisotropic media. Formulated in terms of elastic parameters, the traditional anisotropic ray-tracing systems aredifficult to implement and inefficient for computation, especially for the dynamic ray-tracing system. They may also result inambiguity in specifying elastic parameters for a given medium.To overcome these difficulties, we have reformulated the ray-tracing systems in terms of phase velocity.These reformulated systems are simple and especially useful for general transversely isotropic and weak orthorhombic media, because the phase velocities for these two types of media can be computed with simple analytic expressions. These two types of media also represent the majority of anisotropy observed in sedimentary rocks. Based on these newly developed ray-tracing systems, we have extended prestack Gaussian-beam depth migration to general transversely isotropic media. Test results with synthetic data show that our anisotropic, prestack Gaussian-beam migration is accurate and efficient. It produces images superior to those generated by anisotropic, prestack Kirchhoff migration.

Wave tracing: Ray tracing for the propagation of band-limited signals: Part 2 — Applications

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE385-VE393 ◽  
Author(s):  
John K. Washbourne ◽  
Kenneth P. Bube ◽  
Pedro Carillo ◽  
Carl Addington
Keyword(s):  
Ray Tracing ◽  
Real Data ◽  
Modeling Method ◽  
Simulation Accuracy ◽  
Arrival Times ◽  
Velocity Models ◽  
Depth Images ◽  
Snell’S Law ◽  
Band Limited ◽  
Snell's Law

Modeling seismic propagation is critically important to our work; unfortunately, we often must trade simulation accuracy for reduced computational expense. We present a new seismic-modeling method that is as simple and computationally efficient as Snell’s law ray tracing but provides propagation paths and arrival times more consistent with finite-bandwidth data. We refer to this modeling method as wave tracing and apply it to nonlinear traveltime tomography and depth imaging. By replacing Snell’s law ray tracing with wave tracing, we get better ray coverage, more robust and faster ray bending (fewer iterations), and a much more robust and faster algorithm for nonlinear tomography (fewer iterations, too). A very significant benefit is increased stability and robustness of tomographic inversion with respect to small changes in model parameterization and regularization. A related benefit is the increased stability of depth images with respect to small changes in velocity, which can increase confidence in interpretation. The velocity models that result from wave tracing match picked arrival times in band-limited data better and generate improved depth images. These advantages of wave tracing relative to conventional Snell’s law ray tracing have been tested on both synthetic and real data examples for crosswell seismic geometry.

Comparison of weights in prestack amplitude‐preserving Kirchhoff depth migration

Geophysics ◽  
1997 ◽  
Vol 62 (6) ◽  
pp. 1812-1816 ◽  
Author(s):  
Christian Hanitzsch
Keyword(s):  
Point Source ◽  
Green’S Function ◽  
Ray Tracing ◽  
Green's Function ◽  
Weight Functions ◽  
Depth Migration ◽  
Theoretical Approaches ◽  
Geometric Spreading ◽  
Dynamic Ray Tracing ◽  
Kirchhoff Depth Migration

Three different theoretical approaches to amplitude‐preserving Kirchhoff depth migration are compared. Each of them suggests applying weights in the diffraction stack migration to correct for amplitude loss resulting from geometric spreading. The weight functions are given in different notations, but as is shown, all of these expressions are similar. A notation that is well suited for implementation is suggested: entirely in terms of Green's function quantities (amplitudes or point‐source propagators). For the most common prestack configurations (common‐shot and common‐offset) and 3-D, 2.5-D, and 2-D migrations, expressions of the weights are given in this notation. The quantities needed for calculation of the weights can be computed easily, e.g., by dynamic ray tracing.

Automated stacking of seismic reflection data based on nonrigid image matching

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. V171-V183 ◽  
Author(s):  
Sönke Reiche ◽  
Benjamin Berkels
Keyword(s):  
Image Matching ◽  
Seismic Reflection ◽  
Synthetic Data ◽  
Real Data ◽  
Seismic Line ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Reflection Data ◽  
Seismic Image ◽  
Matching Techniques

Stacking of multichannel seismic reflection data is a crucial step in seismic data processing, usually leading to the first interpretable seismic image. Stacking is preceded by traveltime correction, in which all events contained in a common-midpoint (CMP) gather are corrected for their offset-dependent traveltime increase. Such corrections are often based on the assumption of hyperbolic traveltime curves, and a best fit hyperbola is usually sought for each reflection by careful determination of stacking velocities. However, assuming hyperbolic traveltime curves is not accurate in many situations, e.g., in the case of strongly curved reflectors, large offset-to-target-ratios, or strong anisotropy. Here, we found that an underlying model parameterizing the shape of the traveltime curve is not a strict necessity for producing high-quality stacks. Based on nonrigid image-matching techniques, we developed an alternative way of stacking, both independent of a reference velocity model and any prior assumptions regarding the shape of the traveltime curve. Mathematically, our stacking operator is based on a variational approach that transforms a series of seismic traces contained within a CMP gather into a common reference frame. Based on the normalized crosscorrelation and regularized by penalizing irregular displacements, time shifts are sought for each sample to minimize the discrepancy between a zero-offset trace and traces with larger offsets. Time shifts are subsequently exported as a data attribute and can easily be converted to stacking velocities. To demonstrate the feasibility of this approach, we apply it to simple and complex synthetic data and finally to a real seismic line. We find that our new method produces stacks of equal quality and velocity models of slightly better quality compared with an automated, hyperbolic traveltime correction and stacking approach for complex synthetic and real data cases.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

Finite‐difference calculation of direct‐arrival traveltimes using the eikonal equation

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1270-1274 ◽  
Author(s):  
Le‐Wei Mo ◽  
Jerry M. Harris
Keyword(s):  
Finite Difference ◽  
Ray Tracing ◽  
Finite Differences ◽  
Eikonal Equation ◽  
Velocity Model ◽  
Uniform Grid ◽  
Square Grid ◽  
Kirchhoff Migration ◽  
Difference Calculation

Traveltimes of direct arrivals are obtained by solving the eikonal equation using finite differences. A uniform square grid represents both the velocity model and the traveltime table. Wavefront discontinuities across a velocity interface at postcritical incidence and some insights in direct‐arrival ray tracing are incorporated into the traveltime computation so that the procedure is stable at precritical, critical, and postcritical incidence angles. The traveltimes can be used in Kirchhoff migration, tomography, and NMO corrections that require traveltimes of direct arrivals on a uniform grid.

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