Abstract We adopt a pipeline approach to accelerate 3D time-domain finite-difference waveform inversion codes using graphics cards, thus avoiding domain decomposition. The key designs include streaming through the volume one block at a time and propagating this block as many time steps as possible while it is on the device. This approach allows us to process an arbitrarily large volume with a single GPU, which is particularly suitable in a cloud environment where fast inter-nodal connection is not guaranteed. Moreover, two parameters, block size and number of updates, give users flexibility to adapt to available resources at hand. The most significant advantage that the pipeline approach offers is the ability to compute subsurface offset gathers on GPUs. We describe our implementation on the pseudo-acoustic anisotropic wave equations and show that the pipeline technique achieves nearly linear scaling with number of GPUs. Application to multi-parameter anisotropic inversion on a 3D field data improves the quality of seismic image and flatness of offset gathers.

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GPU上三维时域有限差分多参数波形反演的流水线方法

摘要 我们采用流水线的方法来加速使用显卡的 3D 时域有限差分波形反演码，从而避免域分解。关键设计包括一次一个块地流过卷，并在它在设备上时传播尽可能多的时间步长。这种方式让我们可以用单个 GPU 处理任意大的卷，特别适用于无法保证快速节点间连接的云环境。此外，块大小和更新次数这两个参数使用户可以灵活地适应手头的可用资源。管道方法提供的最显着优势是能够在 GPU 上计算地下偏移道集。我们描述了我们在伪声学各向异性波动方程上的实现，并表明流水线技术实现了与 GPU 数量几乎线性的缩放。3D 野外数据多参数各向异性反演的应用提高了地震图像的质量和偏移道集的平坦度。