滑坡变形动态预测的自记忆离散模型

考虑滑坡变形时间位移序列的非线性特征,提出了基于自记忆离散模型的滑坡非线性变形动态预测方法.该方法将观测到的滑坡变形位移时序数据视为描写滑坡变形非线性动力系统的特解,运用反演动力模式方法导出系统的微分方程,通过引入记忆函数,将制约动力系统的微分方程推演成一个差分-积分方程,从而建立了滑坡变形动态预测的自记忆离散模型.将该方法用于古树屋滑坡和茅坪滑坡变形预测,验证了该模型的有效性及可行性.

Landslide Deformation Dynamic Prediction Based on Self-memorization Discrete Model

Considering the non-linear specificity and monotonic growth characteristics of the time series of landslide deformation,a dynamic prediction method with self-memorization model of landslide deformation is established based on the dynamic data retrieved model and self-memorization equation.By treating the time series data of monitored landslide deformation as the particular solution of the nonlinear dynamic model of landslide deformation,the differential equation describing dynamic characteristics of the landslide deformation system is deduced by using the dynamic model retrieved model.Then,the differential equation is evolved into a differential deduction — integral equation by introducing the memory function to establish a self-memorization model of the dynamic system for predicting nonlinear landslide deformation.The model is applied to predicting the deformation time series data monitored at the Gushuwu landslide and Maoping landslide.The cases show that the self-memorization model is valid and feasible in predicting deformation of landslides.