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Abstract
Building on the concepts of cohesion degree and local relaxation, we propose an integrated hierarchical equilibrium parallel finite-element reverse time migration (HEP-FE-RTM) algorithm, which is a fine-grained central processing unit (CPU) parallel computation method in two-level host-sub-processors mode. A single master process is responsible for data reading and controlling the progress of the calculation, while each subordinate process deals with a part of the depth domain space. This algorithm is able to achieve single source forward-modelling and inversion calculation using more than 2000 CPUs. On the premise of controlling iteration times for convergence, sub-module/processors only communicate with their adjacent counterparts and the host processor, so the level of data exchange is proportional to cohesion degree. This HEP-FE-RTM algorithm has the distinct advantage that parallel efficiency does not decrease as the number of processors increases. In two-level host-sub-processors mode, more than 2000 processors are used and one billion unknowns are solved. By combining the finite-element implicit dynamic Newmark integral scheme, this approach achieves a prestack reverse time migration (RTM) with high expansion. Making full use of the characteristics of high accuracy and strong boundary adaptability of the finite-element method, through the optimisation of finite-element solving, the HEP-FE-RTM algorithm improved the efficiency of parallel computing and achieved RTM implementation using finite element. Model tests show that this method has a significant effect on both imaging efficiency and accuracy.
Building on the concepts of cohesion degree and local relaxation, we propose an integrated hierarchical equilibrium parallel finite-element reverse time migration (HEP-FE-RTM) algorithm, which has the distinct advantage in that parallel efficiency does not decrease as the number of processors increases.
Acknowledgements
We are grateful for the financial support provided by the National Natural Science Foundation of China (No. 41425017). We also thank the Jiangsu Oilfield of SINOPEC for providing us with a synthetic dataset. In addition, we are very grateful to International Science Editing (ISE) for editing this paper. We especially thank the anonymous reviewers and Professor Nori Nakata (University of Oklahoma) for reviewing this manuscript and giving constructive suggestions.