RTM using effective boundary saving: A staggered grid GPU implementation

Marmousi model

The second example is GPU-based RTM for Marmousi model (Figure 7) using our effective boundary saving. The spatial sampling interval is $\Delta x=\Delta z=4m$ . 51 shots are deployed. In each shot, 301 receivers are placed in the split shooting mode. The parameters we use are listed as follows: $nt=13000$ , $\Delta t=0.3$ ms. Due to the limited resource on our computer, we store 65% boundaries using page-locked memory. Figure 8 gives the resulting RTM image after Laplacian filtering. As shown in the figure, RTM with the effective boundary saving scheme produces excellent image: the normalized cross-correlation imaging condition greatly improves the deeper parts of the image due to the illumination compensation. The events in the central part of the model, the limits of the faults and the thin layers are much better defined.

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Figure 7. The Marmousi velocity model.

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Figure 8. RTM result of Marmousi 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