Data assimilation for surface wave method by ensemble Kalman filter with random field modeling

Yuxiang Ren, Shinichi Nishimura, Toshifumi Shibata, Takayuki Shuku

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The ensemble Kalman filter (EnKF) was used to estimate the spatial distribution of the Young's modulus of a model of an earth-fill dam by assimilating the travel time to the first arrival of the surface waves. By the ensemble data assimilation, the measured data from a geophysical exploration was applied to simultaneously estimate the geotechnical properties and evaluate the uncertainties. Swedish weight sounding (SWS) test results were employed as the prior information to generate the initial ensemble through the sequential Gaussian simulation (sGs). In the experiments of assimilation, it was shown that the reproducibility of the parameter field is enhanced by this initial ensemble generation method, and that the uncertainties of the identified parameters can be reduced by the assimilation.

Original languageEnglish
Pages (from-to)2944-2961
Number of pages18
JournalInternational Journal for Numerical and Analytical Methods in Geomechanics
Volume46
Issue number15
DOIs
Publication statusPublished - Oct 25 2022

Keywords

  • data assimilation
  • ensemble Kalman filter
  • sequential Gaussian simulation
  • surface wave method

ASJC Scopus subject areas

  • Computational Mechanics
  • General Materials Science
  • Geotechnical Engineering and Engineering Geology
  • Mechanics of Materials

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