Heterogeneous Data Assimilation for Site Characterization Using the Ensemble Kalman Method

Session: Data Fusion and Uncertainty Quantification in Near-Surface Site Characterization
Type: Oral
Date: 4/30/2020
Time: 08:45 AM
Room: 110 + 140
Description: 

We present an algorithm based on the ensemble Kalman inversion to estimate the near-surface shear wave velocity profile when heterogeneous data sets and a priori information in a form of equality/inequality constraints are available. We use both synthetic and real data to examine the performance of the proposed framework in estimation of Vs profile at the Garner Valley downhole array and compare them against the Vs estimations in previous studies at Garner Valley downhole array. Due to the complementary characteristics of the body and surface waves, we show that formulating the inversion problem using both dispersion data and acceleration time-series can reduce the margins of uncertainty in the Vs profile estimation. We also show how the proposed algorithm can be modified to systematically incorporate constraints that further enhance its well-posedness.

Presenting Author: Elnaz Seylabi

Authors