RTM using effective boundary saving: A staggered grid GPU implementation

Sigsbee model

The last example is Sigsbee model shown in Figure 9. The spatial interval is $\Delta x=\Delta z=25m$ . 55 shots are evenly distributed on the surface of the model. We still perform $nt=13000$ time steps for each shot (301 receivers). Due to the larger model size, 75% boundaries have to be stored with the aid of pinned memory. Our RTM result is shown in Figure 10. Again, the resulting image obtained by normalized cross-correlation imaging condition exhibits better resolution for the edges of the salt body and the diffraction points. Some events in the image using normalized cross-correlation imaging condition are more visible, while they have a much lower amplitude or are even completely lost in the image of cross-correlation imaging condition.

sigsbee
Figure 9. The Sigsbee velocity model.

simag1,simag2
Figure 10. RTM result of Sigsbee model using effective boundary saving scheme (staggered grid finite difference). (a) Result of cross-correlation imaging condition. (b) Result of normalized cross-correlation imaging condition.

RTM using effective boundary saving: A staggered grid GPU implementation