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![]() | RTM using effective boundary saving: A staggered grid GPU implementation | ![]() |
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Assume
-th order finite difference scheme is applied. The Laplacian operator is specified by
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(6) |
Keep in mind that we only need to guarantee the correctness of the wavefield in the original model zone
. However, the saved wavefield in
is also correct. Is it possible to further shrink it to reduce number of points for saving? The answer is true. Our solution is: saving the inner
layers on each side neighboring the boundary
, as shown in Figure 3b. We call it the effective boundary for regular finite difference scheme.
After
steps of forward modeling, we begin our backward propagation with the last 2 wavefield snap
and
and saved effective boundaries in
. At that moment, the wavefield is correct for every grid point. (Of course, the correctness of the wavefield in
is guaranteed.) At time
, we assume the wavefield in
is correct. One step of backward propagation means
is shrunk to
. In other words, the wavefield in
is correctly reconstructed. Then we load the saved effective boundary of time
to overwrite the area
. Again, all points of the wavefield in
are correct. We repeat this overwriting and computing process from one time step to another (
), in reverse time order. The wavefield in the boundary
may be incorrect because the points here are neither saved nor correctly reconstructed from the previous step.
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fig2
Figure 2. 1-D schematic plot of required points in regular grid for boundary saving. Computing the laplacian needs ![]() ![]() ![]() |
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fig3
Figure 3. A 2-D sketch of required points for boundary saving for regular grid finite difference: (a) The scheme proposed by Dussaud et al. (2008) (red zone). (b) Proposed effective boundary saving scheme (gray zone). |
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![]() | RTM using effective boundary saving: A staggered grid GPU implementation | ![]() |
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