[Geodynamics] AGU25 - Call for Abstracts in Session DI002 - Advances in Machine Learning for Solid Earth Geoscience

[XIYUAN BAO]

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://url.au.m.mimecastprotect.com/s/EKjUClx1NjiXWzyG7uGf3izckpE?domain=agu.confex.com


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
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