This article presents a parallel algorithm for seismic imaging based on depth wavefield extrapolation (the solution of a one-way wave equation [OWE]) employing the pseudospectral method. The algorithm is essentially oriented on common-offset vector (COV) gathers seismic migration based on an OWE, includes several parallelism levels, and utilizes message passing interface (MPI), Nested OpenMP, and CUDA technologies. The uppermost level of parallelism involves data splitting to ensure that each dataset can be processed independently to construct parts of the COV images. The algorithm is embarrassingly parallel at this level. Next, each independent run processes several datasets to compute COV images. Each MPI process computes all COV images for a single dataset, then MPI processes exchange data to build up a single COV image for all datasets at a single node. Computation of the COV image at a node requires OpenMP parallelization so that one thread governs the GPU-based calculations and facilitates the construction of images within a thick slab. All other threads perform image interpolation within the slab. Finally, CUDA technology is used for the most computationally intense part of the algorithm—wavefield extrapolation. Such a complex algorithm structure makes it possible to process all COV images for full-azimuth seismic data employing OWE-based migration.

本文提出了一种基于深度波场外推（单向波动方程 [OWE] 的解）的地震成像并行算法，该算法采用伪谱方法。该算法本质上面向基于 OWE 的共偏移向量 (COV) 道集地震偏移，包括多个并行级别，并利用消息传递接口 (MPI)、嵌套 OpenMP 和 CUDA 技术。并行的最高级别涉及数据拆分，以确保可以独立处理每个数据集以构建 COV 图像的一部分。该算法在这个级别上是令人尴尬的并行。接下来，每次独立运行处理多个数据集以计算 COV 图像。每个 MPI 过程计算单个数据集的所有 COV 图像，然后 MPI 处理交换数据以在单个节点上为所有数据集构建单个 COV 图像。节点上 COV 图像的计算需要 OpenMP 并行化，以便一个线程控制基于 GPU 的计算并促进在厚平板内构建图像。所有其他线程在平板内执行图像插值。最后，CUDA 技术用于算法中计算量最大的部分——波场外推。这种复杂的算法结构使得使用基于 OWE 的偏移处理全方位地震数据的所有 COV 图像成为可能。所有其他线程在平板内执行图像插值。最后，CUDA 技术用于算法中计算量最大的部分——波场外推。这种复杂的算法结构使得使用基于 OWE 的偏移处理全方位地震数据的所有 COV 图像成为可能。所有其他线程在平板内执行图像插值。最后，CUDA 技术用于算法中计算量最大的部分——波场外推。这种复杂的算法结构使得使用基于 OWE 的偏移处理全方位地震数据的所有 COV 图像成为可能。