论文标题
具有随机痕量估计的GPU上的基于波浪的反转
Enabling wave-based inversion on GPUs with randomized trace estimation
论文作者
论文摘要
通过在使用随机痕量估计以大大减少伴随状态方法的记忆足迹的最新进展中，我们提出并验证可以专门在加速器上执行的成像方法。包括盐和各向异性在内的现实现实合成数据集获得的结果表明，我们的方法会产生高保真图像。这些发现打开了基于3D Wave的反转技术的诱人观点，其内存足迹与硬件相匹配，并且仅在GPU的群集上运行，而无需不需要的是将某些任务卸载到CPU。
By building on recent advances in the use of randomized trace estimation to drastically reduce the memory footprint of adjoint-state methods, we present and validate an imaging approach that can be executed exclusively on accelerators. Results obtained on field-realistic synthetic datasets, which include salt and anisotropy, show that our method produces high-fidelity images. These findings open the enticing perspective of 3D wave-based inversion technology with a memory footprint that matches the hardware and that runs exclusively on clusters of GPUs without the undesirable need to offload certain tasks to CPUs.
