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A Procedure for Developing Uncertainty-Consistent Vs Profiles From Inversion of Surface Wave Dispersion Data

Session: Data Fusion and Uncertainty Quantification in Shallow Crust Characterization and Modeling

Type: Oral

Date: 4/19/2021

Presentation Time: 05:45 PM Pacific

Description: 

Non-invasive surface wave methods have become a popular alternative to traditional invasive forms of site-characterization for inferring a site’s subsurface shear wave velocity (Vs) structure. The advantage of surface wave methods over traditional forms of site characterization is that measurements made solely at the ground surface can be used routinely and economically to infer the subsurface structure of a site to depths of engineering interest (20-50m), and much greater depths (>1km) in some special cases. However, the quantification and propagation of uncertainties from surface wave measurements into the Vs profiles used in subsequent engineering analyses remains challenging. While this has been the focus of much work in recent years, and while considerable progress has been made, no approach for doing so has been widely accepted, leading analysts to either address the propagation of uncertainties in their own specialized manner or, worse, to ignore these uncertainties entirely. In response, we present a new, effective, and verifiable method for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data. The new approach is shown to be superior to current approaches in two specific ways. First, it is shown to produce suites of Vs profiles whose theoretical dispersion quantitatively reproduces the uncertainties in the experimental dispersion data. Second, beyond appropriate considerations that must be given to all inversions, the method is governed by only one user-defined input parameter, to which it is not overly sensitive. Using two synthetic tests and a real-world example, the procedure is shown to produce suites of Vs profiles that accurately capture the site’s Vs structure, while rigorously propagating the dispersion data’s uncertainty through the inversion process.

Presenting Author: Joseph P. Vantassel

Student Presenter: Yes

Authors