2025-08-12 ローレンスリバモア国立研究所(LLNL)
Scientists at Lawrence Livermore National Laboratory have helped develop an advanced, real-time tsunami forecasting system — powered by El Capitan, the world’s fastest supercomputer — that could dramatically improve early warning capabilities for coastal communities near earthquake zones. (Images courtesy of Tzanio Kolev/LLNL)
＜関連情報＞
- https://www.llnl.gov/article/53276/llnl-scientists-explore-real-time-tsunami-warning-system-worlds-fastest-supercomputer
- https://arxiv.org/abs/2504.16344
極限スケールでのリアルタイムベイジアン推論：カスケディア沈み込み帯への津波早期警報用のデジタルツイン Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone
Stefan Henneking, Sreeram Venkat, Veselin Dobrev, John Camier, Tzanio Kolev, Milinda Fernando, Alice-Agnes Gabriel, Omar Ghattas
arXiv Submitted on 23 Apr 2025
DOI:https://doi.org/10.48550/arXiv.2504.16344
Abstract
We present a Bayesian inversion-based digital twin that employs acoustic pressure data from seafloor sensors, along with 3D coupled acoustic-gravity wave equations, to infer earthquake-induced spatiotemporal seafloor motion in real time and forecast tsunami propagation toward coastlines for early warning with quantified uncertainties. Our target is the Cascadia subduction zone, with one billion parameters. Computing the posterior mean alone would require 50 years on a 512 GPU machine. Instead, exploiting the shift invariance of the parameter-to-observable map and devising novel parallel algorithms, we induce a fast offline-online decomposition. The offline component requires just one adjoint wave propagation per sensor; using MFEM, we scale this part of the computation to the full El Capitan system (43,520 GPUs) with 92% weak parallel efficiency. Moreover, given real-time data, the online component exactly solves the Bayesian inverse and forecasting problems in 0.2 seconds on a modest GPU system, a ten-billion-fold speedup.
