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Data Fusion and Uncertainty Quantification in Near-Surface Site Characterization

Non-invasive methods for site characterization have clear advantages of cost and effort over their invasive counterparts. The inverse problem ill-posedness, however, the inherent complexity of the shallow crust and associated measurement and modeling uncertainties of active and passive surface wave techniques can lead to poor estimations of site properties, which would affect in turn the assessment of earthquake hazard at the site of interest. Recent studies have shown that joint inversion of multiple data-sets recording sub-surface heterogeneities (e.g. active and passive data, ground motion recordings) and statistical inference techniques can improve the estimated properties and better quantify associated uncertainties of non-invasive methods. We here invite contributions on the development and/or implementation of state-of-the-art methods in inverse problems, data assimilation and uncertainty quantification, to improve the characterization of near-surface site conditions.

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

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