[Geodynamics] AGU 26 Call for Abstracts in Session DI001 - Advances in Machine Learning for Solid Earth Geoscience

[XIYUAN BAO]

Dear colleagues,


Please consider submitting an abstract to our AGU 2026 session: DI001 -
Advances in Machine Learning for Solid Earth Geoscience.


With the expansion of observational and experimental datasets, advances in
machine learning methods, and growing computational capabilities, machine
learning is becoming an increasingly important component of Solid Earth
geoscience. These approaches are opening new avenues for investigating
physical and chemical processes across spatial and temporal scales, from
the surface to the deep interior, on Earth and other terrestrial planetary
bodies. This session welcomes contributions spanning a wide range of
methods and applications, including data compilation and mining,
statistical learning, classical and deep neural networks, explainable AI,
generative models, and related approaches applied to geophysics,
geodynamics, geochemistry, structural geology, volcanology, petrology,
mineralogy, and mineral physics. We encourage both methodological
developments tailored to geoscientific problems and application-focused
studies that yield new insights into Solid Earth processes. Example topics
include data mining of geochemical, mineralogical, or volcanological
datasets; deep learning-based geophysical inversion; physics-informed
emulators for geodynamics and landscape evolution; and
machine-learning-assisted multiscale modeling.


The deadline to submit your abstract is Wednesday, 5 August 2026, 23:59 PM
EDT.



Invited Speakers:


Mostafa Mousavi, Harvard University

Daniel O'Malley, Los Alamos National Laboratory



On behalf of conveners,

Xiyuan Bao, Weiqiang Zhu, Shaunna Morrison and Tushar Mittal
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