AI 中文总结
该研究针对地震海啸预警数据集生成成本过高的问题，利用线性时不变结构简化为卷积算子，开发FFT加速的多GPU流水线，在卡斯卡迪亚俯冲带实现了快速波形生成，大幅提升了评估效率。
AI 中文摘要
地震和海啸预警的数据驱动方法依赖于大量破裂场景及其产生的波形集合，但通过重复高保真地震和海啸模拟生成此类数据集的成本过高。我们利用两种动力学的线性时不变结构，预先计算弹性格林函数和声学-重力伴随响应，将震源到波形的映射简化为两个连续的卷积算子。我们使用分布式、FFT加速的GPU流水线评估这些卷积，该流水线将大型海底网格跨GPU分区，直接生成最终观测波形。我们在卡斯卡迪亚俯冲带对该流水线的可扩展性进行了演示，其包含963个子断层、2416530个海底网格点、64个观测位置和256个时间步长，需要9.45 TiB的聚合GPU内存。在一个NVL72域内的64 GB200 GPU上，一旦响应算子驻留，该流水线每次破裂生成波形需24毫秒，可在数分钟内评估大量破裂集合。
英文摘要
Data-driven methods for earthquake and tsunami early warning rely on large ensembles of rupture scenarios and their resulting waveforms, but generating such datasets with repeated high-fidelity seismic and tsunami simulations is prohibitively expensive. We exploit the linear time-invariant structure of both dynamics to precompute elastic Green's functions and acoustic-gravity adjoint responses, reducing the source-to-waveform map to two consecutive convolution operators. We evaluate these convolutions with a distributed, FFT-accelerated GPU pipeline that partitions the large seafloor grid across GPUs and directly generates the final observation waveforms. We demonstrate the scalability of this pipeline for the Cascadia Subduction Zone with 963 subfaults, 2,416,530 seafloor grid points, 64 observation locations, and 256 timesteps, requiring 9.45 TiB of aggregate GPU memory. On 64 GB200 GPUs within one NVL72 domain, the pipeline generates waveforms in 24 ms per rupture once the response operators are resident, enabling large rupture ensembles to be evaluated within minutes.
Comments8 pages, 3 figures
