AVO-Friendly Velocity Analysis Based on the High-Resolution PCA-Weighted Semblance

Applied Sciences

Liyong Fan¹  Chunlin Zhang²  Jijun Li³  Guiting Chen⁴ 

[1] PetroChina Changqing Oilfield Company, Xi’an 710017, China;PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China;School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China;School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;

关键词: seismic velocity;    AB semblance spectrum;    AVO phenomenon;    principal component analysis;    spatial coherence;   

DOI  :  10.3390/app12126098

来源: DOAJ

【 摘 要 】

Velocity analysis using the semblance spectrum can provide an effective velocity model for advanced seismic imaging technology, in which the picking accuracy of velocity analysis is significantly affected by the resolution of the semblance spectrum. However, the peak broadening of the conventional semblance spectrum leads to picking uncertainty, and it cannot deal with the amplitude-variation-with-offset (AVO) phenomenon. The well-known AB semblance can process the AVO anomalies, but it has a lower resolution compared with conventional semblance. To improve the resolution of the AB semblance spectrum, we propose a new weighted AB semblance based on principal component analysis (PCA). The principal components or eigenvalues of seismic events are highly sensitive to the components with spatial coherence. Thus, we utilized the principal components of the normal moveout (NMO)-corrected seismic events with different scanning velocities to construct a weighting function. The new function not only has a high resolution for velocity scanning, but it is also a friendly method for the AVO phenomenon. Numerical experiments with the synthetic and field seismic data sets proved that the new method significantly improves resolution and can provide more accurate picked velocities compared with conventional methods.

【 授权许可】

Unknown