SSA26 Session: Developing Data-Driven Methods in the AI Era: New Approaches to Earthquake Science
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Villa, Valeria
Jan 8, 2026, 12:34:49 PMJan 8
to earthscop...@earthscope.org
Dear colleagues,
We invite abstract submissions to our session titled Developing Data-Driven Methods in the AI Era: New Approaches to Earthquake Science, to be held at the 2026 annual meeting of the Seismological Society of America in Pasadena, California (April 14–18, 2026). The abstract submission deadline is January 13, 2026.
Description: The rapid rise of artificial intelligence and statistical inference is reshaping earthquake science, enabling breakthroughs in earthquake detection, source characterization, forecasting, and earthquake cycle simulations. For example, neural operators have sped up forward modeling and inversion, graph neural networks have successfully tackled a wide range of applications by leveraging the interconnected nature of seismic data, and variational inference has effectively connected data to models in high-dimensional parameter spaces. This session highlights new data-driven approaches that improve upon traditional techniques or introduce new methods entirely. We welcome submissions that bring to light new trends and patterns in old datasets, highlight important signals that are seemingly buried in noise, and speed up scientific analysis when working with large volumes of data. Contributions that demonstrate how AI and statistical methods open fresh pathways for understanding existing problems in earthquake science are of particular interest.
We encourage you to submit your abstract and join the discussion in Pasadena.
Session Conveners:
Susan E. Hough, U.S. Geological Survey (ho...@usgs.gov)
Ian McBrearty, Stanford University (imcb...@stanford.edu)
Mostafa Mousavi, Harvard University (mous...@google.com)
Hongyu Sun, University of Texas at El Paso (hs...@utep.edu)
Valeria Villa, California Institute of Technology (vvi...@caltech.edu)
Taiyi Wang, California Institute of Technology (ta...@caltech.edu)
Clara Yoon, U.S. Geological Survey (cy...@usgs.gov)
Caifeng Zou, California Institute of Technology (cz...@caltech.edu)
We invite abstract submissions to our session titled Developing Data-Driven Methods in the AI Era: New Approaches to Earthquake Science, to be held at the 2026 annual meeting of the Seismological Society of America in Pasadena, California (April 14–18, 2026). The abstract submission deadline is January 13, 2026.
Description: The rapid rise of artificial intelligence and statistical inference is reshaping earthquake science, enabling breakthroughs in earthquake detection, source characterization, forecasting, and earthquake cycle simulations. For example, neural operators have sped up forward modeling and inversion, graph neural networks have successfully tackled a wide range of applications by leveraging the interconnected nature of seismic data, and variational inference has effectively connected data to models in high-dimensional parameter spaces. This session highlights new data-driven approaches that improve upon traditional techniques or introduce new methods entirely. We welcome submissions that bring to light new trends and patterns in old datasets, highlight important signals that are seemingly buried in noise, and speed up scientific analysis when working with large volumes of data. Contributions that demonstrate how AI and statistical methods open fresh pathways for understanding existing problems in earthquake science are of particular interest.
We encourage you to submit your abstract and join the discussion in Pasadena.
Session Conveners:
Susan E. Hough, U.S. Geological Survey (ho...@usgs.gov)
Ian McBrearty, Stanford University (imcb...@stanford.edu)
Mostafa Mousavi, Harvard University (mous...@google.com)
Hongyu Sun, University of Texas at El Paso (hs...@utep.edu)
Valeria Villa, California Institute of Technology (vvi...@caltech.edu)
Taiyi Wang, California Institute of Technology (ta...@caltech.edu)
Clara Yoon, U.S. Geological Survey (cy...@usgs.gov)
Caifeng Zou, California Institute of Technology (cz...@caltech.edu)
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