Emerging Machine Learning, Quantum Computing, and Physics-Based Methods for Geophysical Analysis and Subsurface Characterization
Emerging Machine Learning, Quantum Computing, and Physics-Based Methods for Geophysical Analysis and Subsurface Characterization
Session ID#: 280506
Session Description:
This session invites contributions on recent advances in machine learning and quantum computing in geophysics and Earth and planetary sciences, including their integration with physics-based approaches for inverse analysis, uncertainty quantification, geophysical monitoring, subsurface characterization, and the analysis of complex geoscientific datasets. Topics include, but are not limited to, modeling and inversion in heterogeneous and anisotropic media, macroseismic studies, fracture and reservoir characterization, fluid and rock-property estimation, and monitoring applications, including CO2 sequestration. Contributions involving physics-guided machine learning, data analytics, quantum computing, quantum simulation, and hybrid workflows that combine data-driven and physics-based methods are encouraged. The session aims to foster exchange among researchers developing foundational theory, computational methods, and practical applications for next-generation geophysical analysis, monitoring, and subsurface characterization.
Co-Sponsor(s):
- IN - Informatics
- MR - Mineral and Rock Physics
- NG - Nonlinear Geophysics
- NS - Near Surface Geophysics
Index Terms:
0545 Modeling [COMPUTATIONAL GEOPHYSICS]
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
0902 Computational methods: seismic [EXPLORATION GEOPHYSICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Yujiang Xie, University of Chinese Academy of Sciences, Beijing, China
Convener: Mrinal K Sen, University of Texas at Austin, Austin, United States
See more of: Seismology