Dynamic estimation of geomechanical parameters via ensemble Kalman filter coupled with numerical analysis

被引：0

作者：

Zhao, Hongliang ^{[1
]}

Feng, Xiating ^{[1
]}

Zhang, Dongxiao ^{[2
]}

机构：

[1] State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China

[2] Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma, Norman, OK 73019, United States

来源：

Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering | 2007年 / 26卷 / SUPPL. 2期

关键词：

Algorithms - Deformation - Elastic moduli - Geotechnical engineering - Kalman filters - Monte Carlo methods - Numerical analysis - Optimization - Parameter estimation - Soil mechanics;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

With respect to the uncertainty process, the ensemble Kalman filter (EnKF) is introduced, the geomechanical deformation is treated as a dynamic stochastic system, and the displacement observation is looked as the output to describe the state of system with ensemble Kalmen filter. Furthermore, it is coupled with numerical modeling to cope with the uncertainty. Thus, the dynamical estimation of geomechanical parameters is performed, the parameter and its uncertainty are simultaneously obtained. The numerical examples show that the can effectively deal with the measured data polluted by noise, and can dynamically tract with the mechanical response of rock/soil mass. Compared with the conventional optimization algorithm, the EnKF shows the better character of real time and reliability because it can provide the inversion results and the posteriori distribution of the priori information together.

引用

页码：4130 / 4138