Affiliations: [a] School of Electronics and Information, Yangtze University, Jingzhou 434023, Hubei, China | [b] College of Engineering and Technology, Yangtze University, Jingzhou 434020, Hubei, China | [c] Sgsg Science and Technology Co., Ltd. Zhuhai, Zhuhai 519000, Guangdong, China
Correspondence:
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Corresponding author: Xiang’e Sun, School of Electronics and Information, Yangtze University, Jingzhou 434023, Hubei, China. E-mail: [email protected].
Abstract: Pre-stack Reverse Time migration (RTM) is a full wave field imaging method. There are lots of data calculation tasks and data storage tasks in RTM. This requires powerful data analysis and processing capabilities to meet the needs of actual production. In this paper, we first discuss the seismic data regularization related technology and the impact of the regularized seismic data on the RTM imaging results. Then, in order to improve the efficiency of RTM data processing, cloud computing technology is used. We research the data parallel processing method of RTM in cloud computing environment. We apply the parallel computation of CPU and GPU to RTM, and design a MapReduce parallel computing model C-GMR suitable for multi-GPU nodes. The experimental results show that the technical measures can greatly improve the computational efficiency on the basis of guaranteeing the advantages of RTM high-precision imaging, and provide good technical support for the application of RTM to process massive seismic data.
Keywords: RTM, data regularization, parallel computing, GPU/CPU, cloud
