ATD: Resilient Dynamic Autoencoders for Modeling and Predicting Earthquake Threats — NSF Award to International Computer Science I
Large earthquakes generate strong ground motions and tsunamis that may lead to a significant number of casualties and cause severe impacts on social resilience in seismically active regions including the West Coast of the United States. Early warning systems have been developed to mitigate immediate threats by detectin
| Award title | ATD: Resilient Dynamic Autoencoders for Modeling and Predicting Earthquake Threats |
|---|---|
| Award ID | 2319621 |
| Awardee | International Computer Science Institute |
| City | BERKELEY |
| State | CA |
| Amount obligated | $220,000 |
| Principal investigator | Nils Benjamin Erichson |
| Program | ATD-Algorithms for Threat Dete |
| Start date | 09/01/2023 |
| Abstract | Large earthquakes generate strong ground motions and tsunamis that may lead to a significant number of casualties and cause severe impacts on social resilience in seismically active regions including the West Coast of the United States. Early warning systems have been developed to mitigate immediate threats by detecting first-arriving ground motions near an earthquake epicenter and forecasting the intensity and timing of strong destructive ground motions. To further improve the efficacy and accu |
| Source | NSF Awards |
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