Heterogeneous Data Assimilation for Site Characterization Using the Ensemble Kalman Method
Session: Data Fusion and Uncertainty Quantification in Shallow Crust Characterization and Modeling
Type: Oral
Date: 4/19/2021
Presentation Time: 05:30 PM Pacific
Description:
We present an algorithm based on the ensemble Kalman inversion to estimate the near-surface S- and P-wave velocity profiles when heterogeneous datasets and a priori information in the form of equality/inequality constraints are available. We use synthetic and real data to examine the proposed framework's performance in estimating soil mechanical properties, i.e., S and P wave velocity and damping ratio, at the Garner Valley downhole array in Southern California and compare them against estimates from previous studies at the same site. Due to the complementary characteristics of the body and surface waves, we show that formulating the inversion problem using heterogeneous data (e.g., Rayleigh wave phase velocity, earthquake horizontal-to-vertical spectral ratio, and acceleration time series) can reduce the margins of uncertainty in the estimation. We also show how systematic modifications of the proposed algorithm to incorporate constraints further enhances the algorithm’s well-posedness.
Presenting Author: Elnaz Seylabi
Student Presenter: No
Authors
Elnaz Seylabi Presenting Author Corresponding Author elnaze@unr.edu University of Nevada, Reno |
Elif Bas basel@nevada.unr.edu University of Nevada Reno |
Domniki Asimaki domniki@caltech.edu Caltech |
Andrew Stuart astuart@caltech.edu Caltech |
Alan Yong yong@usgs.gov U.S. Geological Survey |
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Heterogeneous Data Assimilation for Site Characterization Using the Ensemble Kalman Method
Category
Data Fusion and Uncertainty Quantification in Shallow Crust Characterization and Modeling