Collaborative Research: CAIG: Next Generation Machine-Learning Approach to Decode High-Resolution Earthquake Catalogs

Understanding and predicting earthquakes is a critical endeavor that has profound implications for public safety and disaster preparedness. By developing cutting-edge machine learning models and algorithms, this project seeks to uncover the intricate dynamics of earthquakes, potentially identifying precursory signals that precede major seismic events. The broader impact of this work includes enhancing our ability to forecast earthquakes more accurately, thus mitigating risks and improving resilience in communities prone to seismic activities. Additionally, the project will create open-source tools accessible to researchers and practitioners worldwide, fostering global collaboration and knowledge sharing. This initiative not only aims to advance scientific understanding but also to inspire and educate the next generation of geoscientists through engaging sonification/animation products and educational programs. This research addresses the challenge of understanding earthquake physics and forecasting its collective behaviors by utilizing high-resolution, high-dimensional earthquake catalogs and continuous geophysical measurements. Traditional forecasting models struggle with the complexity of such detailed data; thus, this project proposes novel approaches grounded in marked temporal point processes. The key strategies include developing advanced Hawkes process models with deep neural triggering kernels to gain nuanced insights into earthquake dynamics, creating a novel generative framework to explore complex seismic patterns, and applying these methods to recent earthquake sequences in California, Japan, and Türkiye. The project will produce open-source software tools to support these efforts. The intellectual merit lies in integrating advanced statistical models, machine learning techniques, and high-resolution earthquake catalogs to address longstanding challenges in geoscience. By enhancing the representation of earthquake dynamics with deep neural triggering kernels within Hawkes process models, the project aims to overcome limitations of traditional forecasting methods. The generative framework for marked temporal point processes will enable systematic exploration of intricate seismic patterns. International collaborations and the development of accessible, open-source resources exemplify a commitment to impactful and practical research. Additionally, the project will offer Research Experiences for Undergraduates (REU) at Georgia Tech, promoting interdisciplinary collaboration and broadening participation in geosciences. The collaboration between an early career machine learning PI and a mid-career earthquake seismologist further underscores the project's innovative and interdisciplinary nature. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.