像域分解特征高斯波包地震数据模拟

分波型或时空局部特征波场的层析成像与叠前深度偏移成像组合是全波形反演成像方法(FWI)实用化的途径之一.其中如何高效稳健地获得具有时空局部特征的地震数据目前仍然是一个挑战.在前人提出的基于高斯束和高斯波包的Gabor框架散射波模拟的基础上, 本文提出了一种基于像域分解的特征高斯波包地震数据模拟方法.
新方法利用具有空间局部特征的Gabor函数在成像剖面上实现数据的分解, 不仅可以利用成像剖面地质意义更加直观的特点来拾取特征反射界面, 还能有效地克服传统方法在数据域分解效率和精度不高等问题, 保证了分解后的反射数据具有更加明确的地质意义.此外, 不同于波动方程有限差分模拟, 基于时空局部特征的高斯波包模拟的一次反射波数据包含更多的数据属性, 比如反射波的到达时、空间位置和波场的传播方向, 这在层析反演中用于数据测量和偏移成像等处理时更具优势.数值实验测试表明了本文提出的分解方法的有效性.

Seismic data modeling of characteristic Gaussian wave packet based on image domain decomposition

The combination of wave-mode-based or spatiotemporally local-characteristic-wavefield-based tomography and prestack depth migration is one of the ways to apply full waveform inversion (FWI). How to efficiently and steadily obtain seismic data with local temporal and spatial characteristics is still a challenge. Based on the Gabor frame scattering wave simulation of Gaussian beam and Gaussian wave packet proposed by predecessors, a characteristic Gaussian wave packet seismic data modeling method based on image domain decomposition is proposed in this paper. The new method uses Gabor function with local spatial features to decompose the data on the imaging section.
It can not only pick up the characteristic reflection interface by using the more intuitive geological significance of the imaging section, but also effectively overcome the low decomposition efficiency and accuracy of the traditional methods in the data domain, and ensure that the decomposed reflection data has more clear geological significance. In addition, different from the finite difference simulation of wave equation, the seismic data simulated by Gaussian wave packet based on spatiotemporal local characteristics contains more data attributes, such as arrival time of reflected wave, spatial position and propagation direction of wavefield, which has more advantages in data measurement and migration in tomography inversion. Numerical experiments show the effectiveness of the decomposition method proposed in this paper.