Primary Convener:
Xiyuan Bao, Harvard University

Convener:
Jie Deng, Princeton University
Karianne Bergen, Brown University
Maurizio Petrelli, University of Perugia

Early Career Convener:
Caifeng Zou, California Institute of Technology

Chair:
Xiyuan Bao, University of California Los Angeles
Jie Deng, Princeton University
Karianne Bergen, Stanford University
Maurizio Petrelli, University of Perugia
Caifeng Zou, California Institute of Technology

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.

Index Terms
0555 Neural networks, fuzzy logic, machine learning
1027 Composition of the planets
3919 Equations of state
7290 Computational seismology

Cross-Listed:
T - Tectonophysics
MR - Mineral and Rock Physics
V - Volcanology‚ Geochemistry and Petrology
S - Seismology

Suggested Itineraries:
Machine Learning and AI
Open Science and Open Data

Neighborhoods:
2. Earth Interior