基于集合卡尔曼滤波的岩土力学参数动态估计

 针对非确定性过程，引入集合卡尔曼滤波(EnKF)理论，视岩土变形体为一个随机动态系统，将位移观测值作为系统的输出，用集合卡尔曼滤波模型来描述系统的状态；进一步耦合数值分析方法实现岩土力学参数的随机动态估计，在有效地获得待估参数的同时还给出估计值的不确定性。通过数值算例表明，集合卡尔曼滤波可以有效地对含噪声的量测数据进行处理，能够跟踪岩土力学行为的动态变化。对比于常用最优化算法，集合卡尔曼滤波同时给出反演结果和先验知识的后验分布，显示出更好的实时性和可靠性。

DYNAMIC ESTIMATION OF GEOMECHANICAL PARAMETERS VIA ENSEMBLE KALMAN FILTER COUPLED WITH NUMERICAL ANALYSIS

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.