Towards Efficient Reverse-time Migration Imaging Computation by Pipeline and Fine-grained Execution Parallelization

The reverse-time migration (RTM) imaging algorithm is widely used in petroleum seismic exploration analysis. It is one of the most accurate imaging algorithms but is also computation-intensive and thus time-consuming. In this paper, we focus on improving the parallel execution performance of the reverse-time migration imaging algorithm. Firstly, we analyze the performance bottlenecks of the reverse-time migration imaging algorithm with program profiling techniques. Based on the program profiling and performance analysis, we propose three effective performance improvement strategies, including the pipeline-based iterative propagation computation, the fine-grained data compression, and the GPU memory specification-based data transmission, to eliminate the performance bottle-necks. Extensive experiments on physical clusters and real-world datasets show that the proposed pipeline-based and fine-grained parallel RTM algorithm can reduce the running time by an average of 58.42% compared with the existing solutions. In addition, the proposed algorithm has been used for over one year in the real-world production environment in Sinopec, which is one of the world's largest petroleum exploration companies.