Project Grant G25AP00408
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $300,001 to Trustees of Boston University to develop a foundational Artificial Intelligence (AI) model for advanced seismic data analysis to improve earthquake detection, localization, and characterization. The project aims to revolutionize earthquake science by using AI to unravel patterns in seismic data, leading to more accurate tools for earthquake monitoring and potential prediction....
- This federal Project Grant award of $386,220, provided by the National Science Foundation's Geosciences Program (CFDA 47.050), supports the development of machine learning techniques to efficiently generate realistic, synthetic earthquake ground motions. The research, conducted by the University of California, San Diego, aims to enable geologists to effectively study large earthquakes and assess the hazards they pose to California and Nevada. The project will produce publicly available...
- This $175,000 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports research to develop an AI model for understanding fault dynamics and earthquake hazards in complex fault networks. The project, led by the California Institute of Technology (Caltech), aims to build a multiphysics fault network model to discover reduced-order governing equations for the evolution of stress in fault systems. This work will enable improved assessment of regional...
- This $100,000 Project Grant award under the U.S. Geological Survey's (USGS) Earthquake Hazards Program Assistance (CFDA 15.807) will support a collaborative research project between the University of Hawaii Manoa and the USGS. The primary objectives are to: Produce InSAR deformation and coherence time series data for multiple locations in California to analyze the spatial correlation between long-term ground displacement related to human activities and ground failure (liquefaction and lateral...
- This National Science Foundation (NSF) Project Grant award under the Geosciences Program (CFDA 47.050) provides $299,539 to the Georgia Tech Research Corporation to develop advanced machine learning models and algorithms to study the intricate dynamics of earthquakes. The key objectives are to uncover precursory signals that may precede major seismic events, enhance earthquake forecasting capabilities, and create open-source tools accessible to researchers and practitioners worldwide. The...
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $650,000 over a 3-year period to the University of Southern California to develop an AI model that can better understand fault dynamics and earthquake hazards in heavily faulted geologic basins. The project builds a multiphysics fault network model to discover reduced-order governing equations for the evolution of stress in complex fault systems, using the Southern Permian Basin in the...
- This $218,952.63 project grant was awarded by the U.S. Geological Survey (USGS) under the Earthquake Hazards Program Assistance (CFDA 15.807) to the University of Bristol. The grant will fund research to develop neural and Bayesian point process models for earthquake forecasting, with the goal of improving upon existing community earthquake forecasting standards such as the Epidemic Type Aftershock Sequence (ETAS) model. The project will benchmark the performance of these new AI-based models...
- This National Science Foundation (NSF) Project Grant under the Geosciences program (CFDA 47.050) will provide $392,914 to Carnegie Mellon University (CMU) from October 1, 2024 to September 30, 2027 to develop a collaborative research project titled "CAIG: Next Generation Machine-Learning Approach to Decode High-Resolution Earthquake Catalogs." The project aims to leverage advanced machine learning models and algorithms to gain deeper insights into earthquake dynamics and improve...
- This National Science Foundation (NSF) Geosciences program (CFDA 47.050) award of $249,272 to the University of Nevada, Reno (UNR) supports the development of machine learning techniques to generate realistic, synthetic earthquake ground motion simulations. The goal is to enable geologists to efficiently study large, infrequent earthquakes and assess their hazards to California and Nevada. The project will create publicly available software and educational resources to train future scientists...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $220,000 Project Grant to the International Computer Science Institute (ICSI), a non-profit research organization, under the Mathematical and Physical Sciences program (CFDA 47.049). The project aims to develop resilient and reliable deep learning methods for forecasting complex spatiotemporal ground motion data, with applications in seismology, earth sciences, and other domains. Key technical objectives include...
This $100,000 Project Grant awarded by the U.S. Geological Survey (USGS) under the Earthquake Hazards Program Assistance (CFDA 15.807) supports the development of a novel AI-enabled framework called WaveCastNet-V2 for forecasting earthquake ground motions. The International Computer Science Institute (ICSI), a non-profit research organization in Berkeley, California, will lead this collaborative research effort with the University of California, Berkeley. The key products and services to be delivered under this grant include: (1) a robust AI-based WaveCastNet-V2 framework for earthquake ground motion forecasting applicable to a wide range of magnitudes, (2) a pre-trained AI model for the San Francisco Bay Area, (3) a curated real waveform database for earthquakes, and (4) a synthetic waveform database for training and validation. The research aims to improve the accuracy of earthquake early warning systems by reducing false and missing warnings that can arise from inaccuracies in rapid earthquake magnitude and location estimation.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $100.0k | 8/20/25 |