Improving prestack time migration by introducing a new velocity-related parameter: Parameter picking and 3D real data application

GEOPHYSICS(2024)

引用 0|浏览0
暂无评分
摘要
Prestack time migration (PSTM), a commonly used tool for seismic imaging, has been widely applied in 3D seismic data processing. However, the conventional PSTM algorithms use only one effective velocity parameter (i.e., root -mean -square [rms] velocity) for each imaging point, which may not be accurate when stronger lateral variations occur in seismic velocities. In this paper, we introduce a new parameter called the velocity variation factor that considers velocity variations in inhomogeneous media to improve PSTM. This new parameter, together with the rms velocity, describes the propagation Green's function at an imaging point with two effective parameters rather than one effective parameter as in conventional PSTMs. This provides a more accurate traveltime calculation for the wave propagating through media with moderate lateral velocity variation. Unlike the conventional bending -ray PSTM, the additional effective parameter is fully independent of the rms velocities. We estimate the two effective parameters at each imaging point by flattening the neighboring image gathers with a global optimization algorithm. The objective function is built at each imaging point using a selective crosscorrelation-based time shift, which can quantitatively describe the slight bending of events in the local migrated gathers regardless of the quality of the gathers. We estimate the two effective parameters using the very fast simulated annealing algorithm and multiscale approach, thus avoiding the local minimum caused by the noises in the migrated gathers. We apply our twoparameter PSTM to a real 3D land data set to demonstrate its industrial applicability. A comparison of the new imaging result with the conventional prestack depth migration also is presented.
更多
查看译文
关键词
prestack time migration,3d real data application,parameter picking,velocity-related
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要