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Date: 4/19/2021

Session Time: 5:00 PM to 6:15 PM Pacific

Data Fusion and Uncertainty Quantification in Shallow Crust Characterization and Modeling

Non-invasive methods for sub-surface seismic site characterizations have clear advantages relating to cost and effort over their invasive counterparts. However, these methods rely on several constituents that can lead to poor estimations of sub-surface properties, which significantly affect earthquake hazard assessment at the regional scale. The limiting factors include the ill-posedness of inverse problems, the shallow crust's inherent complexity and measurement and modeling uncertainties. Recent studies have shown that joint inversion of complementary datasets and statistical inference techniques can improve the estimated properties and quantify uncertainties. We here invite contributions describing the development and implementation of state-of-the-art methods in inverse problems, data assimilation and uncertainty quantification to improve constraints on shallow crust properties. We also invite contributions to the development of three-dimensional shallow crust velocity models using the methodologies mentioned above, as well as other novel approaches.

Conveners

Elnaz Seylabi, University of Nevada, Reno (elnaze@unr.edu)
Domniki Asimaki, California Institute of Technology (domniki@caltech.edu)
Alan Yong, U.S. Geological Survey (yong@usgs.gov)
Nori Nakata, Massachusetts Institute of Technology (nnakata@mit.edu)

Oral Presentations