Separation of split shear waves based on a hodogram analysis of HTI media

Acta Geophysica

Yun Wang¹  Yuyong Yang¹  Jun Lu¹ 

[1] China University of Geosciences

关键词: Wave propagation;    Time-series analysis;    Seismic anisotropy;    Fracture;    Image processing;   

DOI  :  10.1007/s11600-018-0172-8

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

Although the shear-wave birefringence phenomenon affects the imaging of converted shear waves, it also provides a considerable amount of information on subsurface fracture development. Therefore, it is significant to separate split shear waves before seismic interpretation and reservoir prediction. In this paper, we propose a new method of split shear waves separation based on the polarization directions derived from hodogram analysis. Through the hodogram analysis, we find that the split shear-wave particle motions are within the range of a specific and fixed rectangle, which have relations with the fracture azimuth in strata. In addition, we found that a couple of split shear waves can only be fitted to the unique trajectory rectangle through the theoretical derivation. Based on this, we establish the trajectory rectangle through the wave vector calculation and calculate the fracture azimuth according to the fact that the one edge of the trajectory rectangle is along or perpendicular to the fracture azimuth. Synthetic data analysis shows that the calculation accuracy of fracture azimuth under the constraint of trajectory rectangle is less affected by the time delay between split shear waves than using the method of eigenvector–eigenvalue decomposition (EED). Therefore, we can obtain better results for separation of split shear waves using our method than using EED. Eventually, we propose an approach of layer stripping to deal with the problem that shear wave split several times due to the situation that different strata have different fracture azimuths. Synthetic data test indicates that our method can achieve higher calculation efficiency and faster convergence speed than the conventional eigenvector–eigenvalue decomposition method, even though the data are of a low signal-to-noise ratio. Moreover, field data applications show the effectiveness and potential of our method.

【 授权许可】

Unknown