Collaborative Research: CAIG: Multi-Task and Multi-Scale Deep Learning Inversion for Geoph — NSF Award to University of Texas at D
Understanding the Earth’s subsurface is essential for society’s ability to monitor natural hazards, manage environmental risks, and support sustainable energy development. Seismic waves generated by earthquakes and other sources can provide clues about underground structures, but interpreting these waves remains comple
| Award title | Collaborative Research: CAIG: Multi-Task and Multi-Scale Deep Learning Inversion for Geoph |
|---|---|
| Award ID | 2530788 |
| Awardee | University of Texas at Dallas |
| City | RICHARDSON |
| State | TX |
| Amount obligated | $193,567 |
| Principal investigator | Yapeng Tian |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 01/01/2026 |
| Abstract | Understanding the Earth’s subsurface is essential for society’s ability to monitor natural hazards, manage environmental risks, and support sustainable energy development. Seismic waves generated by earthquakes and other sources can provide clues about underground structures, but interpreting these waves remains complex and computationally intensive. This project will advance artificial intelligence (AI) methods for imaging and monitoring the Earth’s subsurface. By combining advances in geophysi |
| Source | NSF Awards |
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