Shear‐wave splitting: Tutorial, issues and implications for 9‐C 3‐D seismic reflection data

Author(s):  
James L. Simmons ◽  
Milo M. Backus
Geophysics ◽  
1993 ◽  
Vol 58 (2) ◽  
pp. 240-256 ◽  
Author(s):  
Xiang‐Yang Li ◽  
Stuart Crampin

Most published techniques for analyzing shear‐wave splitting tend to be computing intensive, and make assumptions, such as the orthogonality of the two split shear waves, which are not necessarily correct. We present a fast linear‐transform technique for analyzing shear‐wave splitting in four‐component (two sources/ two receivers) seismic data, which is flexible and widely applicable. We transform the four‐component data by simple linear transforms so that the complicated shear‐wave motion is linearized in a wide variety of circumstances. This allows various attributes to be measured, including the polarizations of faster split shear waves and the time delays between faster and slower split shear waves, as well as allowing the time series of the faster and slower split shear waves to be separated deterministically. In addition, with minimal assumptions, the geophone orientations can be estimated for zero‐offset verticle seismic profiles (VSPs), and the polarizations of the slower split shear waves can be measured for offset VSPs. The time series of the split shear‐waves can be separated before stack for reflection surveys. The technique has been successfully applied to a number of field VSPs and reflection data sets. Applications to a zero‐offset VSP, an offset VSP, and a reflection data set will be presented to illustrate the technique.


2021 ◽  
Author(s):  
Barbara Dietiker ◽  
André J.-M. Pugin ◽  
Matthew P. Griffiths ◽  
Kevin Brewer ◽  
Timothy Cartwright

<p>Based on our experience, one of the most important steps in processing shear-wave seismic reflection data is the velocity analysis. In unconsolidated materials a very fine velocity analysis is more essential for S-waves than for P-waves because shear-wave velocities vary over several orders of magnitude and can change very quickly laterally and with depth. Velocities between 100m/s in glaciolacustine/marine deposits (clay-sized silts) and 1200m/s in stiff diamicton (till) were encountered in recent surveys. Shear-wave velocities have the large advantage of not being changed by the phase of the pore content such as the groundwater table.</p><p>We present two fundamentally different methods for velocity determination: 1) velocity semblance analysis based on hyperbolic reflection move-out on common midpoint (cmp) gathers and 2) Local Phase – Local Shift (LPLS) method which automatically estimates the reflection slope (local static shift) in the time-frequency domain of cmp gathers. Published in 2020, the latter method can be used for automated processing and substantially saves processing time.</p><p>Processing steps in preparation for velocity analysis (independent of the chosen method) include frequency filtering, trace equalizing and muting. We show velocity semblance images from different geological settings (glacial, postglacial) and from different shear components and discuss differences. Information gained besides shear velocities include mapped reflectors and located diffractions. Using those examples, we demonstrate how combining all information using visualisation techniques enhances interpretation of such data sets.</p>


Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. G35-G45 ◽  
Author(s):  
Laura Valentina Socco ◽  
Daniele Boiero ◽  
Sebastiano Foti ◽  
Roger Wisén

Seismic reflection data contain surface waves that can be processed and interpreted to supply shear-wave velocity models along seismic reflection lines. The coverage of seismic reflection data allows the use of automated multifold processing to extract high-quality dispersion curves and experimental uncertainties in a moving spatial window. The dispersion curves are then inverted using a deterministic, laterally constrained inversion to obtain a pseudo-2D model of the shear-wave velocity. A Monte Carlo global search inversion algorithm optimizes the parameterization. When the strategy is used with synthetic and field data, consistent final models with smooth lateral variations are successfully retrieved. This method constitutes an improvement over the individual inversion of single dispersion curves.


2006 ◽  
Vol 55 (3) ◽  
pp. 129-139 ◽  
Author(s):  
Avihu Ginzburg ◽  
Moshe Reshef ◽  
Zvi Ben-Avraham ◽  
Uri Schattner

Data Series ◽  
10.3133/ds496 ◽  
2009 ◽  
Author(s):  
Janice A. Subino ◽  
Shawn V. Dadisman ◽  
Dana S. Wiese ◽  
Karynna Calderon ◽  
Daniel C. Phelps

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