Data assimilation for surface wave method by ensemble Kalman filter with random field modeling. (9th August 2022)

• Record Type: Journal Article
• Authors: Ren, Yuxiang; Nishimura, Shinichi; Shibata, Toshifumi; Shuku, Takayuki
• Is Part Of: International journal for numerical and analytical methods in geomechanics. Volume 46:Number 15(2022)
• Page Start: 2944
• Page End: 2961
• Publication Date: 2022-08-09
• Subjects: data assimilation -- ensemble Kalman filter -- sequential Gaussian simulation -- surface wave method; Soil mechanics -- Mathematics -- Periodicals; Rock mechanics -- Mathematics -- Periodicals; 624.1510151
• DOI: 10.1002/nag.3435
• Languages: English
• ISSNs: 0363-9061

Abstract

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.