CAIG: A Bayesian Inference Framework for Learning Earthquake Cycle Deformation Processes A — NSF Award to University of Texas at A
Advances in space- and ground-based monitoring have allowed scientists to improve the characterization of tectonic plate boundaries which show deformation across a vast range of spatial and temporal scales. Most dramatically, such deformation includes destructive earthquakes along faults. The exact stressors triggering
| Award title | CAIG: A Bayesian Inference Framework for Learning Earthquake Cycle Deformation Processes A |
|---|---|
| Award ID | 2425922 |
| Awardee | University of Texas at Austin |
| City | AUSTIN |
| State | TX |
| Amount obligated | $832,277 |
| Principal investigator | Omar Ghattas |
| Program | GEO CI - GEO Cyberinfrastrctre, MSPA-INTERDISCIPLINARY |
| Start date | 09/01/2024 |
| Abstract | Advances in space- and ground-based monitoring have allowed scientists to improve the characterization of tectonic plate boundaries which show deformation across a vast range of spatial and temporal scales. Most dramatically, such deformation includes destructive earthquakes along faults. The exact stressors triggering earthquakes along these faults remains uncertain despite a wealth of observational data and a strong understanding of the basic physics involved. This project will employ a branch |
| Source | NSF Awards |
$799/mo
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