Improving the memory efficiency of RTM using both Nyquist sampling and DCT based on GPU

Abstract

Abstract Reverse time migration (RTM) is used to obtain complex structural images of subsurface media. The RTM can be expressed as a zero-lag cross-correlation between the source and receiver wave fields. As imaging conditions can be calculated based on the pre-stored source wavefield and the received wavefield generated during backward modelling, only the source wavefield must be stored in the memory (or disc). Therefore, reducing source-wavefield storage requirements can improve memory efficiency. High-performance computing based on graphic processing units (GPUs) is being developed to reduce the computational time in wave-propagation modelling. Accordingly, GPU-based RTM technology has the potential to improve the computational efficiency of RTM. Storage of the source wavefield wholly in GPU video random-access memory (VRAM) may further improve computational efficiency. In this paper, we present a new algorithm for a three-dimensional (3D) GPU-based RTM that can enable efficient storage of the source wavefield in VRAM using both Nyquist sampling and discrete cosine transform (DCT) compression. A numerical example employing a modified SEG/EAGE 3D overthrust model presented in this study verifies that the proposed algorithm requires only 2% of the memory usage of conventional RTM while producing similar results.