RTM using effective boundary saving: A staggered grid GPU implementation

• RTM via effective boundary saving is proposed for regular grid and staggered grid finite difference.
• CPML boundary condition is combined with staggered grid finite difference to minimize storage requirement.
• The GPU codes are provided for implementation and reproducible research.
• The Marmousi and Sigsbee models are used to demonstrate the validity of our method.

GPU has become a booming technology in reverse time migration (RTM) to perform the intensive computation. Compared with saving forward modeled wavefield on the disk, RTM via wavefield reconstruction using saved boundaries on device is a more efficient method because computation is much faster than CPU–GPU data transfer. In this paper, we introduce the effective boundary saving strategy in backward reconstruction for RTM. The minimum storage requirement for regular and staggered grid finite difference is determined for perfect reconstruction of the source wavefield. Particularly, we implement RTM using GPU programming, combining staggered finite difference scheme with convolutional perfectly matched layer (CPML) boundary condition. We demonstrate the validity of the proposed approach and CUDA codes with numerical example and imaging of benchmark models.