Parallel Scalability of a Fine-Grain Prestack Reverse Time Migration Algorithm

Abstract

Seismic imaging has evolved significantly due to the high demand from the oil/gas industry for hardware technological advancements, boosting the development of more sophisticated algorithms. In order to deliver the quality and accuracy required, the execution of these algorithms may lead to time infeasible solutions. Aiming at performance improvement, this work conducted the parallelization of the core of a reverse time migration (RTM) algorithm. Furthermore, analysis such as speedup and efficiency was performed in order to assess the scalability of the proposed method. While the many parallelization efforts so far deal with coarse-grain approaches, this letter tackles the intrashot fine-grain parallelization of prestack RTM, which increases the overall concurrency degree of the algorithm. Results using 2-D synthetic data show that the proposed approach is scalable, which means that an increase in hardware resources and/or in problem size will lead to a proportional increase in speed and/or accuracy.