Practical Evaluation of P- and S-Wave Separation Via Elastic Wavefield Decomposition

2006 ◽  
Vol 37 (2) ◽  
pp. 139-149 ◽  
Author(s):  
Natasha Hendrick ◽  
Emma Brand
Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1972-1982 ◽  
Author(s):  
Remco Muijs ◽  
Klaus Holliger ◽  
Johan O. A. Robertsson

Dense spatial recording patterns of three‐component (3C) receivers allow for direct wavefield decomposition through explicit calculation of divergence and curl of the recorded elastic wavefield. Since this approach is based upon the observation of small phase shifts, it requires highly accurate deployment of the receiver configurations. To study the feasibility of a recently proposed P/S‐wave separation scheme, we systematically assess the effects of position and orientation errors of one or several geophones within the recording pattern on technique performance. We find that realistic deployment errors can significantly affect estimates of the divergence and curl of particle velocity. The errors induced by mispositioned or misoriented geophones differ for each of the geophones that make up a pattern. Moreover, the inaccuracies vary with the angle of incidence, potentially affecting analysis procedures applied to the data at a later stage, such as amplitude variation with offset (AVO). Based on a relative L1‐criterion, the position of each receiver needs to be accurate within 10% of the length of the sides of the configuration to obtain meaningful divergence and curl estimates. Furthermore, the output is particularly sensitive to misorientations of geophones, requiring that the orientations of all geophones be accurate within 2°. These observations point to significant difficulties when applying this technique. To alleviate this problem, we present an approach to detect and compensate for such deployment‐related inaccuracies prior to explicit P/S‐wave separation. This strategy is based on a pyramid‐shaped receiver configuration and relies on minimizing the differences between the divergence and curl estimates calculated over the pyramid and each of the four subtetrahedra that comprise the pyramid.


Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. S569-S577 ◽  
Author(s):  
Yang Zhao ◽  
Houzhu Zhang ◽  
Jidong Yang ◽  
Tong Fei

Using the two-way elastic-wave equation, elastic reverse time migration (ERTM) is superior to acoustic RTM because ERTM can handle mode conversions and S-wave propagations in complex realistic subsurface. However, ERTM results may not only contain classical backscattering noises, but they may also suffer from false images associated with primary P- and S-wave reflections along their nonphysical paths. These false images are produced by specific wave paths in migration velocity models in the presence of sharp interfaces or strong velocity contrasts. We have addressed these issues explicitly by introducing a primary noise removal strategy into ERTM, in which the up- and downgoing waves are efficiently separated from the pure-mode vector P- and S-wavefields during source- and receiver-side wavefield extrapolation. Specifically, we investigate a new method of vector wavefield decomposition, which allows us to produce the same phases and amplitudes for the separated P- and S-wavefields as those of the input elastic wavefields. A complex function involved with the Hilbert transform is used in up- and downgoing wavefield decomposition. Our approach is cost effective and avoids the large storage of wavefield snapshots that is required by the conventional wavefield separation technique. A modified dot-product imaging condition is proposed to produce multicomponent PP-, PS-, SP-, and SS-images. We apply our imaging condition to two synthetic models, and we demonstrate the improvement on the image quality of ERTM.


2016 ◽  
Vol 13 (3) ◽  
pp. 500-510 ◽  
Author(s):  
Jia-Jia Yang ◽  
Xi-Wu Luan ◽  
Gang Fang ◽  
Xin-Xin Liu ◽  
Jun Pan ◽  
...  

2000 ◽  
Author(s):  
R. Muijs ◽  
K. Holliger ◽  
J.O.A. Robertsson
Keyword(s):  
S Wave ◽  

Author(s):  
Yanwen Wei ◽  
Yunyue Elita Li ◽  
Jingjing Zong ◽  
Jizhong Yang ◽  
Haohuan Fu ◽  
...  

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