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 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 of $386,220 from the National Science Foundation's Geosciences Program (CFDA 47.050) will support the development of machine learning techniques to generate realistic, synthetic earthquake ground motion data. The key products of this project include: Physics-based machine learning models, named Operator Inference (OPINF), that can create time-dependent parametric surrogate models of seismic ground motions by fusing simulated wavefields with observed earthquake records....
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 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $462,400 to the University of California San Diego, Scripps Institution of Oceanography, to develop advanced machine learning techniques for generating realistic, synthetic earthquake ground motion simulations. The goal is to create a faster, more generalizable physics-based model that can efficiently study large, infrequent earthquakes and assess their hazards for California and Nevada....
This National Science Foundation (NSF) Geosciences Program (CFDA 47.050) Project Grant award of $249,272, effective January 1, 2026 through December 31, 2028, supports the development of machine learning techniques to generate realistic, synthetic ground motion simulations for studying large earthquakes. The project, conducted by the University of Nevada, Reno (UNR), aims to create a physics-based, parametric surrogate model called Operator Inference (OPINF) that can produce seismic ground...
This $589,708 Project Grant award from the National Science Foundation's Geosciences program (CFDA 47.050) supports research to advance artificial intelligence (AI) methods for imaging and monitoring the Earth's subsurface. The project aims to develop a multi-task deep learning inversion framework that can simultaneously estimate subsurface velocity structures and earthquake source parameters using passive seismic data. By integrating deep learning with conventional full-waveform inversion...
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 Project Grant award from the National Science Foundation (CFDA 47.050 Geosciences Program) provides $452,604 to President and Fellows of Harvard College to develop a foundational AI model for advanced seismic data analysis. The project aims to revolutionize earthquake science by using AI to identify patterns in seismic data and gain a deeper understanding of earthquake characteristics. The model will be trained on a vast archive of seismic data to improve earthquake detection, localization,...
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 Project Grant award of $218,952.63 from the U.S. Geological Survey's (USGS) Earthquake Hazards Program Assistance (CFDA 15.807) will support the development and benchmarking of neural point process models for improved earthquake forecasting. The key deliverables include:
Establishing a community benchmarking exercise for evaluating existing neural and other point process models for earthquake forecasting.
Extending the identified neural models to incorporate magnitude information and re-assessing their predictive capabilities.
Comparing the enhanced neural point process models against the USGS UCERF3-ETAS model and a novel approximate Bayesian ETAS model.
The award recipient, the University of Bristol, will leverage its expertise in seismology and machine learning to pursue these activities, with the goal of meaningfully connecting AI advancements to operational earthquake forecasting needs. The project period runs from August 1, 2025 to July 31, 2026.