Session Outline

Multidisciplinary and Interdisciplinary (M)
Session Sub-category		Technology & Techniques (TT)
Session ID		M-TT47
Title		Interdisciplinary Studies on Pre-Earthquake Processes with Advanced Models, Observations, and AI
Short Title		Pre-Earthquake Processes
Main Convener	Name	Yangkang Chen
	Affiliation	University of Texas at Austin
Co-Convener 1	Name	John B Rundle
	Affiliation	University of California Davis
Co-Convener 2	Name	Katsumi Hattori
	Affiliation	Department of Earth Sciences, Graduate School of Science, Chiba University
Co-Convener 3	Name	Peng Han
	Affiliation	Southern University of Science and Technology, Shenzhen, China
Co-Convener 4	Name	Dimitar Ouzounov
	Affiliation	Chapman University
Session Language		E
Scope		This session highlights recent advances in interdisciplinary research on pre-earthquake processes that combine space- and ground-based observations, physical modeling, and artificial intelligence (AI). Diverse observables, such as deformation (GPS, InSAR), electromagnetic, geochemical, hydrogeological, and ionospheric changes, have been linked to stress evolution in the lithosphere before large earthquakes. The emergence of large language models (LLMs), generative AI, and multi-modal learning frameworks is transforming this field. These tools enable fusion of heterogeneous datasets, generation of synthetic precursors for model testing, and real-time interpretation of complex signals through in-context and physics-informed learning. We invite contributions on observational and modeling results; machine learning and AI-enhanced methods for earthquake forecasting and nowcasting; multi-modal data integration; explainable and physics-informed AI; and advanced sensing and computational technologies. The session aims to connect geophysical, geochemical, and computational communities to accelerate progress toward more reliable, interpretable, and physically grounded earthquake forecasting systems.
Session Format		Orals and Posters session