Calibrating Imperfect Geophysical Models by Fusing Data from Multiple Sources

• Activity Number: 90 - Invited EPoster Session
• Type: Invited
• Date/Time: Sunday, July 28, 2019 : 8:30 PM to 10:30 PM
• Sponsor: ASA
• Author(s): Mengyang Gu*
• Companies: Johns Hopkins University
• Keywords: Discrepancy function; Data fusion; Effective sample size; Measurement bias; Scaled Gaussian stochastic process

Abstract

Model calibration or data inversion involves using experimental or field data to estimate the unknown parameters in a mathematical model. In this work, we calibrate the geophysical model by integrating different types of field data, such as interferometric synthetic aperture radar satellite (InSAR) interferograms, GPS, velocities of tilt and lava lake from the Kilauea Volcano during the eruption in 2018. This task is complicated by the discrepancy between the model and reality, the different samples and possible bias in field data. We introduce the scaled Gaussian stochastic process (S-GaSP), a new stochastic process to model the discrepancy function in calibration for the identifiability issue between the calibrated mathematical model and the discrepancy function. We study the difference between the aggregated data and full data in calibration, and provide a feasible way to fuse field data from multiple sources with different sample sizes. The calibration models are implemented in the "RobustCalibration" R Package on CRAN.