Dear colleagues, Please consider submitting an abstract to our AGU 2025 session: DI002 - Advances in Machine Learning for Solid Earth Geoscience at the following link: https://urldefense.us/v3/__https://agu.confex.com/agu/agu25/prelim.cgi/Sessi... With the rapid growth of observational datasets, advancements in machine learning algorithms, and expanding computational power, machine learning is playing an increasingly significant role in Solid Earth Geosciences. These tools are transforming our understanding of physical and chemical processes across spatial and temporal scales, both on Earth and other planetary bodies.This session invites contributions across a broad spectrum of methods and applications, including but not limited to: data compilation and mining, statistical modeling, classical and deep learning, explainable AI, and generative models applied to geophysics, geodynamics, geochemistry, structural geology, volcanology, petrology, and mineral physics. We encourage both novel machine learning methodologies addressing geoscientific challenges and innovative applications that provide new insights into Earth processes. Example topics include dimensionality reduction or clustering analysis on geochemical or volcanological data, uncertainty-aware geophysical inversion, seismic event detection with neural networks, geodynamical emulators, and machine-learning assisted multiscale modeling. The deadline to submit your abstract is July 30, 2025. Invited Speakers: Bill White, Cornell University Xiaodong Song, Peking University On behalf of conveners, Xiyuan Bao, Jie Deng, Karianne Bergen, Maurizio Petrelli & Caifeng Zou _______________________________________ Xiyuan Bao Daly Postdoctoral Fellow Department of Earth and Planetary Sciences Harvard University