Collaborative Research: CAIG: Space-time completeness of seismic ground motions via non-in — NSF Award to Board of Regents, NSHE,
Large earthquakes occur infrequently, often separated from each other by decades or centuries. This makes it difficult for scientists to predict and understand large earthquakes because they have only a few historical examples to base their models on. To get around this limitation, scientists use computer simulations t
| Award title | Collaborative Research: CAIG: Space-time completeness of seismic ground motions via non-in |
|---|---|
| Award ID | 2531037 |
| Awardee | Board of Regents, NSHE, obo University of Nevada, Reno |
| City | RENO |
| State | NV |
| Amount obligated | $249,272 |
| Principal investigator | Daniel Trugman |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 01/01/2026 |
| Abstract | Large earthquakes occur infrequently, often separated from each other by decades or centuries. This makes it difficult for scientists to predict and understand large earthquakes because they have only a few historical examples to base their models on. To get around this limitation, scientists use computer simulations to create “synthetic” earthquakes that they can study. Unfortunately, these synthetic earthquakes currently require thousands of hours to compute. In this project, machine learning |
| Source | NSF Awards |
$799/mo
Try NSFGrants →