Transformational Regional-Scale Earthquake Simulations with the DOE EarthQuake SIMulation Exascale Framework

Journal Article · 07 May 2024 · Computing in Science and Engineering

DOI: https://doi.org/10.1109/mcse.2024.3397768 · OSTI ID:2477237

McCallen, David  ^[1]; Pitarka, Arben  ^[2]; Tang, Houjun  ^[1]; Pankajakshan, Ramesh  ^[2]; Petersson, Anders  ^[2]; Miah, Mamun  ^[1]

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1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Earthquakes present worldwide risk to economic and human safety. The 2023 earthquakes in Turkiye provided a reminder of the potential for catastrophic consequences with 50,700 deaths and 15.7 million people affected. The ability to predict ground motions and infrastructure damage for earthquakes continues to be a challenging problem for scientists and engineers. Until now, estimates of ground motions have been performed empirically by looking at sparse data from past earthquakes. This approach can provide statistical information on intensity amplitudes but cannot inform site-specific ground motions essential to developing the most effective resilience. Interest has grown in large-scale computational models to simulate earthquakes at regional scale. The U.S. Department of Energy EarthQuake SIMulation (EQSIM) framework was developed for regional-scale earthquake simulations at unprecedented fidelity, taking advantage of emerging GPU-accelerated systems. This article describes the EQSIM workflow and demonstrates regional-scale simulations with the new computational capability available to scientists in their quest to mitigate future disasters.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC02-05CH11231; AC52-07NA27344
• OSTI ID: 2477237
• Report Number(s): LLNL-JRNL-2001742; ark:/13030/qt6fc7j3wk
• Journal Information: Computing in Science and Engineering, Journal Name: Computing in Science and Engineering Journal Issue: 2 Vol. 26; ISSN 1521-9615
• Publisher: IEEE Computer SocietyCopyright Statement
• Country of Publication: United States
• Language: English